Communication error alerting in an epilepsy monitoring system
Systems and methods for monitoring neurological signals in a patient are provided. The system includes: an implantable sensor adapted to collect neurological signals; an implantable assembly configured to sample the neurological signals collected by the sensor; and a rechargeable communication device external to the patient's body, said communication device configured to wirelessly communicate with the implantable assembly and to transmit a communication error alert to a caregiver advisory device in the event of a communication error between the implantable assembly and the communication device.
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The present application is a continuation of pending U.S. patent application Ser. No. 12/020,507, filed Jan. 25, 2008, which claims benefit of U.S. Provisional Patent Application No. 60/897,551, filed Jan. 25, 2007, the disclosures of which are incorporated by reference herein in their entirety.BACKGROUND OF THE INVENTION
The present invention relates generally to systems and methods for sampling and processing one or more physiological signals from a subject. More specifically, the present invention relates to monitoring of one or more neurological signals from a subject to determine a subject's susceptibility to a neurological event, communicating the subject's susceptibility to the subject, reducing a severity of seizures and/or preventing seizures. The invention also relates to continuously storing neurological signals from a subject to train algorithms to determine a subject's susceptibility for having a seizure.
Epilepsy is a neurological disorder of the brain characterized by chronic, recurring seizures. Seizures are a result of uncontrolled discharges of electrical activity in the brain. A seizure typically manifests itself as sudden, involuntary, disruptive, and often destructive sensory, motor, and cognitive phenomena. Seizures are frequently associated with physical harm to the body (e.g., tongue biting, limb breakage, and burns), a complete loss of consciousness, and incontinence. A typical seizure, for example, might begin as spontaneous shaking of an arm or leg and progress over seconds or minutes to rhythmic movement of the entire body, loss of attention, loss of consciousness, and voiding of urine or stool.
A single seizure most often does not cause significant morbidity or mortality, but severe or recurring seizures (epilepsy) results in major medical, social, and economic consequences. Epilepsy is most often diagnosed in children and young adults, making the long-term medical and societal burden severe for this population of subjects. People with uncontrolled epilepsy are often significantly limited in their ability to work in many industries and usually cannot legally drive an automobile. An uncommon, but potentially lethal form of seizure is called status epilepticus, in which a seizure continues for more than 30 minutes. This continuous seizure activity may lead to permanent brain damage, and can be lethal if untreated.
While the exact cause of epilepsy is often uncertain, epilepsy can result from head trauma (such as from a car accident or a fall), infection (such as meningitis), stroke, or from neoplastic, vascular or developmental abnormalities of the brain. Approximately 70% of epileptic subjects, especially most forms that are resistant to treatment (i.e., refractory), are idiopathic or of unknown causes, and is generally presumed to be an inherited genetic disorder.
Demographic studies have estimated the prevalence of epilepsy at approximately 1% of the population, or approximately 2.5 million individuals in the United States alone. In order to assess possible causes and to guide treatment, epileptologists (both neurologists and neurosurgeons) typically evaluate subjects with seizures with brain wave electrical analysis and imaging studies, such as magnetic resonance imaging (MRI).
While there is no known cure for epilepsy, chronic usage of anticonvulsant and antiepileptic medications can control seizures in most people. For most cases of epilepsy, the disease is chronic and requires chronic medications for treatment. The anticonvulsant and antiepileptic medications do not actually correct the underlying conditions that cause seizures. Instead, the anticonvulsant and antiepileptic medications manage the subject's epilepsy by reducing the frequency of seizures. There are a variety of classes of antiepileptic drugs (AEDs), each acting by a distinct mechanism or set of mechanisms.
AEDs generally suppress neural activity by a variety of mechanisms, including altering the activity of cell membrane ion channels and the susceptibility of action potentials or bursts of action potentials to be generated. These desired therapeutic effects are often accompanied by the undesired side effect of sedation, nausea, dizziness, etc. Some of the fast acting AEDs, such as benzodiazepine, are also primarily used as sedatives. Other medications have significant non-neurological side effects, such as gingival hyperplasia, a cosmetically undesirable overgrowth of the gums, and/or a thickening of the skull, as occurs with phenytoin. Furthermore, some AED are inappropriate for women of child bearing age due to the potential for causing severe birth defects.
An estimated 70% of subjects will respond favorably to their first AED monotherapy and no further medications will be required. However, for the remaining 30% of the subjects, their first AED will fail to fully control their seizures and they will be prescribed a second AED—often in addition to the first—even if the first AED does not stop or change a pattern or frequency of the subject's seizures. For those that fail the second AED, a third AED will be tried, and so on. Subjects who fail to gain control of their seizures through the use of AEDs are commonly referred to as “medically refractory.” This creates a scenario in which 750,000 subjects or more in the United States have uncontrolled epilepsy. These medically refractory subjects account for 80% of the $12.5 billion in indirect and direct costs that are attributable to epilepsy in the United States.
A major challenge for physicians treating epileptic subjects is gaining a clear view of the effect of a medication or incremental medications. Presently, the standard metric for determining efficacy of the medication is for the subject or for the subject's caregiver to keep a diary of seizure activity. However, it is well recognized that such self-reporting is often of poor quality because subjects often do not realize when they have had a seizure, or fail to accurately record seizures.
If a subject is refractory to treatment with chronic usage of medications, surgical treatment options may be considered. If an identifiable seizure focus is found in an accessible region of the brain, which does not involve “eloquent cortex” or other critical regions of the brain, then resection is considered. If no focus is identifiable, or there are multiple foci, or the foci are in surgically inaccessible regions or involve eloquent cortex, then surgery is less likely to be successful or may not be indicated. Surgery is effective in more than half of the cases, in which it is indicated, but it is not without risk, and it is irreversible. Because of the inherent surgical risks and the potentially significant neurological sequelae from resective procedures, many subjects or their parents decline this therapeutic modality.
Some non-resective functional procedures, such as corpus callosotomy and subpial transection, sever white matter pathways without removing tissue. The objective of these surgical procedures is to interrupt pathways that mediate spread of seizure activity. These functional disconnection procedures can also be quite invasive and may be less effective than resection.
An alternative treatment for epilepsy that has demonstrated some utility is open loop Vagus Nerve Stimulation (VNS). This is a reversible procedure which introduces an electronic device which employs a pulse generator and an electrode to alter neural activity. The vagus nerve is a major nerve pathway that emanates from the brainstem and passes through the neck to control visceral function in the thorax and abdomen. VNS uses open looped, intermittent stimulation of the left vagus nerve in the neck in an attempt to reduce the frequency and intensity of seizures. See Fisher et al., “Reassessment: Vagus nerve stimulation for epilepsy, A report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology,” Neurology 1999; 53:666-669. While not highly effective, it has been estimated that VNS reduces seizures by an average of approximately 30-50% in about 30-50% of subjects who are implanted with the device. Unfortunately, a vast majority of the subjects who are outfitted with the Cyberonics® VNS device still suffer from un-forewarned seizures and many subjects obtain no benefit whatsoever.
Another recent alternative electrical stimulation therapy for the treatment of epilepsy is deep brain stimulation (DBS). Open-loop deep brain stimulation has been attempted at several anatomical target sites, including the anterior nucleus of the thalamus, the centromedian nucleus of the thalamus, and the hippocampus. The results have shown some potential to reduce seizure frequency, but the efficacy leaves much room for improvement.
Another type of electrical stimulation therapy for the treatment epilepsy has been proposed by NeuroPace, Inc., in which an implanted device is designed to detect abnormal electrical activity in the brain and respond by delivering electrical stimulation to the brain.
There have also been a number of proposals described in the patent literature regarding the use of predictive algorithms that purportedly can predict the onset of a seizure. When the predictive algorithm predicts the onset of a seizure, some type of warning is provided to the subject regarding the oncoming seizure or some sort of therapy is initiated. For example, see U.S. Pat. No. 3,863,625 to Viglione, U.S. Pat. No. 5,995,868 to Dorfmeister/Osorio, and U.S. Pat. No. 6,658,287 to Litt et al., the complete disclosures of which are incorporated herein by reference, describe a variety of proposed seizure prediction systems. However, to date, none of the proposed seizure prediction systems have shown statistically significant results.
While most seizures are short-lasting events that last only a few minutes, the seemingly random nature of the occurrence of seizures is what overshadows and destroys a subject's quality of life.SUMMARY
Systems and methods for monitoring neurological signals in a patient are provided. The system includes: an implantable sensor adapted to collect neurological signals; an implantable assembly configured to sample the neurological signals collected by the sensor; and a rechargeable communication device external to the patient's body, said communication device configured to wirelessly communicate with the implantable assembly and to transmit a communication error alert to a caregiver advisory device in the event of a communication error between the implantable assembly and the communication device.
Also provided are methods and systems for sampling one or more physiological signals from the subject and processing such physiological signal(s) to monitor a subject's susceptibility or for a future neurological event. Such systems may also be adapted to provide an indication to the subject of their susceptibility for the neurological event, such as a warning or instruction, automatically initiate delivery of therapy to the subject, or allow or instruct the subject or a caregiver to administer a therapy prior to the onset of the seizure.
In preferred embodiments, the present invention is for managing epilepsy. Managing epilepsy includes the prevention or reduction of the occurrence of epileptic seizures and/or mitigating their effects, as well as alerting a subject when their susceptibility for having a seizure has been determined to be low. The method of preventing an epileptic seizure comprises characterizing a subject's susceptibility or susceptibility for a future seizure, and upon the determination that the subject has an elevated susceptibility for the seizure, communicating to the subject and/or a health care provider a warning or a therapy recommendation and/or initiating a therapy.
In one embodiment, the present invention provides ambulatory data collection systems and methods. The data collection systems of the present invention typically include one or more electrodes for sampling one or more physiological signals from the subject. In some embodiments, it may be desirable to include microelectrodes. In preferred embodiment, the physiological signals include signals that are indicative of neural activity in at least one portion of the brain, such as intracranial EEG (“iEEG” or “ECoG”), EEG, or a combination thereof. The electrodes may be intracranial electrodes (e.g., epidural, subdural, depth electrodes), extracranial electrodes (e.g., spike or bone screw electrodes, subcutaneous electrodes, scalp electrodes, dense array (256 channels) electrodes, etc.), or a combination thereof. While it is preferred to monitor signals directly from the brain, it may also be desirable to monitor brain activity using sphlenoidal electrodes, foramen ovale electrodes, intravascular electrodes, peripheral nerve electrodes, cranial nerve electrodes, or the like. While the remaining disclosure focuses on intracranial electrodes, it should be appreciated that any type of electrodes may be used to sample signals from the subject.
The one or more electrodes are typically in communication with an implanted assembly. The one or more electrodes may communicate with the implanted assembly (or directly with the external assembly as described below) with a wireless link, a wired link, or both. The implanted assembly is typically configured to facilitate transmission of a data signal that is representative of the one or more sampled physiological signals. The implanted assembly may be in wireless communication with an external assembly using any type of known uni-directional or bi-directional wireless link. Transmission of data and/or control signals between implantable assembly and the external assembly is typically carried out through a radiofrequency link, but may also be carried out through telemetry, magnetic induction, electromagnetic link, Bluetooth® link, Zigbee link, sonic link, optical link, other types of conventional wireless links, or combinations thereof.
In one embodiment, the external assembly will typically be configured to establish a one-way or two-way communication link with the implanted assembly using conventional telemetry handshaking protocols. The external assembly may allow the subject (or the subject's physician) to adjust parameters of the sampling of the physiological signal—such as adjusting the sampling rate, the data transmission rate, error correction, sampled channels, signal conditioning parameters (gain, filtering bandwidth, etc.), the type of data that is stored, or the like. In some embodiments, the implanted assembly will transmit a data signal that includes raw or processed physiological signal (e.g., intracranial EEG, EEG, etc.), one or more features that are extracted from the one or more signals, a signal that is indicative of a communication that is provided to the subject (e.g., warning, therapy recommendation, etc.) or a combination thereof.
At least one of the implanted assembly and external assembly may have a memory sub-system for storing data that is representative of the one or more physiological signals that are sampled with the one or more electrodes. In preferred embodiments, the data is stored in the memory sub-system of the external assembly. The data stored in the memory sub-system of the external assembly may thereafter be transferred to a FLASH drive, hard drive, a local computer, or to a remote server or computer system through a network connection (e.g., local area network (LAN), wide area network (WAN), the Internet, or the like). Preferably, the data will be transmitted to the subject's physician or computer station that is running software that can analyze the subject's physiological signals.
In some embodiments, at least one of the implanted assembly and external assembly will include one or more algorithms for analyzing the sampled physiological signal in real time. Such algorithms may be used as a seizure advisory system that is configured to measure the subject's susceptibility for having a neurological symptom. The systems of the present invention will comprise similar elements as the data collection system described above to facilitate sampling of EEG signals (and/or other physiological signals) from the subject that are indicative of the subject's susceptibility to seizure. The EEG signals may be analyzed by one or more analysis algorithms to determine when a subject is in an ictal state, a pro-ictal state or a contra-ictal state. An “ictal state” is used herein to refer to a seizure. The term “pro-ictal” is used herein to refer to a neurological state or condition characterized by an increased likelihood or higher susceptibility of transitioning to an ictal state. The term “contra-ictal” is used herein to refer to a neurological state or condition characterized by a low likelihood or susceptibility of transitioning to an ictal state and/or a pro-ictal state within a predetermined period of time. A more complete description of pro-ictal, contra-ictal and ictal states are described in co-pending and commonly owned patent application Ser. No. 12/020,450, filed Jan. 25, 2008, to Snyder et al., entitled “Systems and Methods for Identifying a Contra-ictal Condition in a Subject,” the complete disclosure of which is incorporated herein by reference.
In one embodiment, a subject's susceptibility for a seizure can be estimated or derived from a neural condition which can be characterized as a point along a single or multi-variable state space continuum. The term “neural state” is used herein to generally refer to calculation results or indices that are reflective of the state of the subject's neural system, but does not necessarily constitute a complete or comprehensive accounting of the subject's total neurological condition. The estimation and characterization of “neural state” may be based on one or more subject dependent parameters from the brain, such as electrical signals from the brain, including but not limited to electroencephalogram signals “EEG” and electrocorticogram signals “ECoG” or intracranial EEG (referred to herein collectively as EEG″), brain temperature, blood flow in the brain, concentration of AEDs in the brain or blood, etc.), heart rate, respiratory rate, chemical concentrations, etc.
The algorithms may analyze the sampled EEG signals in the implanted assembly, in the external assembly, or a portion of the advisory algorithm may be in both the implanted assembly and the external assembly. If the seizure advisory algorithm determines that the subject has entered a pro-ictal condition, the external assembly may be used to provide a warning, instruction, or other output to the subject that informs them of their transitioning from an inter-ictal or normal condition to the pro-ictal condition. The output from the external assembly may be visual, audio, tactile (e.g., vibratory), or some combination thereof. Such outputs from the external assembly may allow the user to make themselves safe (e.g., stop cooking, pull to the side of the road when driving, lie down, etc.) prior to the onset of the actual seizure or allow the subject to take an acute dosage of an AED to prevent or mitigate the seizure. Most importantly, the subject's will no longer be surprised by the seizures and will have more control over their life.
Such algorithms may also be used to provide insight to the subject and the subject's physician regarding the subject's specific seizure triggers. For example, if the subject's susceptibility to a seizure increases (and a warning is given) every time the subject intakes alcohol or a specific food, is sleep deprived, or is subject to a certain stimulus, the subject may be able to learn which triggers to avoid. Consequently, such seizure advisory systems will be able to provide quantifiable data to the subject and their physician regarding the subject-specific seizure triggers.
The seizure detection algorithm(s) may be used to detect the electrographic seizure onset and provide a seizure warning to the subject (or a care giver) just prior to the clinical manifestation of the seizure. Such a warning may or may not be sufficient to allow the subject to stop the seizure from occurring, but at a minimum, the warning will provide the subject or caregiver many seconds (or minutes) prior to the onset of the clinical seizure and allow the subject and/or caregiver to make the subject safe.
The systems described herein can also include an alert that is configured to indicate that there is a communication error between the implanted assembly and the external assembly. The alert can be disposed either in the internal assembly or in the external assembly. The alert can be a visible alert, an audible alert, a tactile alert, or any combination thereof.
The communication error can be a single type of communication error, or it can be a combination of different types of communication errors. For example, the communication error can be that the external assembly is out of communication range with the implanted assembly such that the external assembly is not receiving a data signal from the implanted device. The communication error can be that the external assembly is out of communication range with the implanted assembly for a predetermined amount of time. The communication error can be that the external assembly not receiving the data signal at an expected time or within an expected period of time. The external assembly can be configured to expect to receive a substantially continuous data signal, or the external assembly can be programmed to expect to receive a data signal periodically. The communication error can be that there is a gap in a data signal communication stream, such as missing packets of data in a numbered sequence of packets. The communication error can also be a data formatting error, such as an invalid cyclic redundancy. If the system detects a communication error an alert will be activated to indicate there is a communication error.
The systems that provide an alert when there is a communication error between the implanted assembly and the external assembly can also include an input on the external assembly that allows the subject to deactivate an alert function when the subject has a low likelihood of transitioning into the seizure condition, such as a contra-ictal condition. An alarm deactivation period can be less than a time period in which the subject is unlikely to transition into the seizure condition. In one example, the deactivation period is 45 minutes and the time period in which the subject is unlikely to transition into the seizure condition is 60 minutes. The deactivation period can be adjustable by the subject up to a maximum time period that does not exceed the time period in which the subject is unlikely to transition into the seizure condition.
Another aspect of the invention is a seizure advisory device. The seizure advisory device includes a user interface that comprises an indicator that indicates if the subject is at a low susceptibility to a seizure or a high susceptibility to a seizure. The seizure advisory device also includes an alert that is configured to provide an indication, such as an audible output, to the subject if the seizure advisory device is out of communication range with an implantable telemetry unit and is unable to accurately communicate the subject's susceptibility to the seizure.
One aspect of the invention is a method of activating an alert when there is a communication error between an implantable device and a device external to a subject. The method includes sampling a brain activity signal from a subject, transmitting a data signal indicative of the sampled brain activity signal from an implanted assembly to an external assembly outside of the subject, and activating an alert when there is a communication error between the implanted assembly and the external assembly. The method can include storing the data signal in the implanted assembly if there is a communication error, as well as attempting to retransmit the data signal after the error is detected.
One aspect of the invention is a method of informing a subject when a seizure advisory device is out of communication range with an implantable device. The method includes receiving a transcutaneously transmitted data signal indicative of the sampled brain activity signal from an implanted device and activating an alert when an expected data signal transmitted from the implanted device is not received by the seizure advisory device. The seizure advisory device can be configured to analyze the data signal to estimate the subject's susceptibility to a seizure. Alternatively, the implanted device is configured to analyze the data signal to estimate the subject's susceptibility to a seizure and the seizure advisory device is configured to communicate the estimated susceptibility to the subject. The data signal can be transmitted substantially continuously and comprises substantially real-time sampled brain activity signals.
The seizure advisory systems of the present invention may be used in conjunction with a therapy that may prevent the seizure from occurring, reduce the severity of the oncoming seizure, reduce the duration of the oncoming seizure, or the like. The therapy may be initiated in a closed loop within the system, or the therapy may be manually initiated by the subject or caregiver.
Depending on the level of the subject's susceptibility for a seizure, the output provided to the subject may take a variety of different forms. Some embodiments will provide an output to the subject that causes the subject to take an acute dosage of a pharmacological agent (e.g., neuro-suppressant, sedative such as a rapid onset benzodiazepine, AED or anticonvulsant, or other medication which exhibits seizure prevention effects). The advisory algorithm(s) may be used to characterize the subject's susceptibility for a future seizure. If the advisory algorithm determines that the subject is at an increased or elevated susceptibility for a future seizure, the system may provide an output to the subject and/or caregiver that facilitates the subject to take or the caregiver to provide an acute dosage of a pharmacological agent (such as an AED) to prevent the occurrence of the seizure or reduce the magnitude or duration of the seizure.
As used herein, the term “anti-epileptic drug” or “AED” generally encompasses pharmacological agents that reduce the frequency or susceptibility of a seizure. There are many drug classes that comprise the set of AEDs, and many different mechanisms of action are represented. For example, some medications are believed to increase the seizure threshold, thereby making the brain less likely to initiate a seizure. Other medications retard the spread of neural bursting activity and tend to prevent the propagation or spread of seizure activity. Some AEDs, such as the benzodiazepines, act via the GABA receptor and globally suppress neural activity. However, other AEDs may act by modulating a neuronal calcium channel, a neuronal potassium channel, a neuronal NMDA channel, a neuronal AMPA channel, a neuronal metabotropic type channel, a neuronal sodium channel, and/or a neuronal kainite channel.
Unlike conventional anti-epileptic drug treatments, which provide for an “open loop” chronic regimen of pharmacological agents, the present invention is able to manage seizures acutely while substantially optimizing the intake of the pharmacological agent by having the subject to take a pharmacological agent only when it is determined that the subject has transitioned to a higher susceptibility to a seizure, e.g., to a pro-ictal condition. Furthermore, with this new paradigm of seizure prevention, the present invention provides a new indication for pharmacotherapy. This new indication is served by several existing medications, including AEDs, given at doses which are sub-therapeutic to their previously known indications, such as acute AED administration for seizure termination or status epilepticus. Since this new indication is served by a new and much lower dosing regimen and consequently a new therapeutic window, the present invention is able to provide a correspondingly new and substantially reduced side effect profile and may reduce or eliminate tolerance effects of the AED. For example, the present invention allows the use of dosages that are lower than FDA-approved dosages for the various anti-epileptic agents. This dosing may be about 5% to about 95% lower than the FDA-recommended dose for the drug, and preferably at or below 90% of the FDA-recommended dose, and most preferably below about 50% of the FDA-recommended dose. But as can be appreciated, if the measured signals indicate a high susceptibility for a seizure, the methods and systems of the present invention may recommend taking an FDA or a higher than FDA approved dose of the AED to prevent the seizure. Such a paradigm has valuable application for subjects in which side effects of AEDs are problematic, particular sedation in general and teratogenicity in pregnant women or risk of teratogenicity in all women of child bearing age. A more complete description of using acute dosages of AEDs with a seizure advisory system is described in commonly owned U.S. patent application Ser. Nos. 11/321,897, 11/321,898, and 11/322,150 (all filed Dec. 28, 2005), the complete disclosures of which are incorporated herein by reference.
In another embodiment, the present invention provides a system that comprises an advisory algorithm that may be used to modify or alter the scheduling and/or dosing of a chronically prescribed pharmacological agent, such as an AED, to optimize or custom tailor the dosing to a particular subject at a particular point in time. This allows for (1) improved efficacy for individual subjects, since there is variation of therapeutic needs among subjects, and (2) improved response to variation in therapeutic needs for a given subject with time, resulting form normal physiological variations as well as from external and environmental influences, such as stress, sleep deprivation, the presence of flashing lights, alcohol intake and withdrawal, menstrual cycle, and the like The advisory algorithm may be used to characterize the subject's susceptibility for the future seizure. If the advisory algorithm determines that the subject is at an elevated susceptibility for an epileptic seizure or otherwise predicts the onset of a seizure, the system may provide an output that indicates or otherwise recommends or instructs the subject to take an accelerated or increased dosage of a chronically prescribed pharmacological agent. Consequently, the present invention may be able to provide a lower chronic plasma level of the AED and modulate the intake of the prescribed agent in order to decrease side effects and maximize benefit of the AED.
The seizure advisory systems of the present invention may be used by medically refractory subjects as well as by subjects who are chronically administering one or more AEDs. Advantageously, such a system may be used to titrate the chronic medications to a level that reduces the side effects, while still providing seizure prevention effects. If the seizure advisory systems of the present invention are able to determine that the subject has a high susceptibility with time periods that are longer than the time for the AED to reach a threshold plasma level and prevent the onset of the seizure, the subject may be able to take supplementary dosage of medication that is administered in response to the assessment of the higher susceptibility to the seizure. Such a method would reduce the subject's overall chronic intake of AEDs, while still preventing seizures in the subject.
The supplementary dosages may be the subject's standard dosage, a larger than standard dosage, or a smaller than standard dosage. The supplementary dosage could be the same AED that the subject takes chronically, or it could be a different AED. It may be desirable to have the dosage and/or type of medication be variable based on the ability of the algorithms to assess the particular subject's neurological condition. While the above description focuses on subject-administered AEDs, the systems of the present invention also encompass the use of implanted drug pumps that be automatically initiated by a control signal from the implanted assembly and/or the external assembly. Such implanted drug pumps may use similar dosing schemes as described above.
In other embodiments, the present invention provides seizure advisory systems in conjunction with automated or manual actuation of electrical neuromodulation. The electrical neuromodulation may be delivered to a peripheral nerve (e.g., vagus nerve stimulation (“VNS”)), a cranial nerve (e.g., trigeminal nerve stimulation (“TNS”)), directly to the brain tissue (e.g., deep brain stimulation (DBS), cortical stimulation, etc.), or any combination thereof.
In one configuration, the seizure advisory systems of the present invention may be used in conjunction with an existing implanted Cyberonics® VNS system. If the seizure advisory system of the present invention determines that the subject has transitioned to a pro-ictal condition, the system may provide an output to the subject so as to inform the subject to activate the VNS device with a wand. In alternative embodiments, the implanted assembly may include an integrated pulse generator that is configured to generate the neuromodulation signal that is delivered to a vagus nerve electrode.
Advantageously, the systems and methods of the present invention may be used to reduce subject anxiety and restore a sense of control in the subject's life, stop or reduce the duration or severity of the seizures, reduce or eliminate physical injuries to the subject, potentially increase vocational opportunities by allowing epileptic subjects to hold down jobs they wouldn't otherwise be able to have, resume their driving privileges, increase comfort with social interaction, and enable certain key activities of daily living.
For a further understanding of the nature and advantages of the present invention, reference should be made to the following description taken in conjunction with the accompanying drawings.
The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:
Certain specific details are set forth in the following description and figures to provide an understanding of various embodiments of the invention. Certain well-known details, associated electronics and devices are not set forth in the following disclosure to avoid unnecessarily obscuring the various embodiments of the invention. Further, those of ordinary skill in the relevant art will understand that they can practice other embodiments of the invention without one or more of the details described below. Finally, while various processes are described with reference to steps and sequences in the following disclosure, the description is for providing a clear implementation of particular embodiments of the invention, and the steps and sequences of steps should not be taken as required to practice this invention.
The term “condition” is used herein to generally refer to the subject's underlying disease or disorder—such as epilepsy, depression, Parkinson's disease, headache disorder, etc. The term “state” is used herein to generally refer to calculation results or indices that are reflective a categorical approximation of a point (or group of points) along a single or multi-variable state space continuum of the subject's condition. The estimation of the subject's state does not necessarily constitute a complete or comprehensive accounting of the subject's total situation. As used in the context of the present invention, state typically refers to the subject's state within their neurological condition. For example, for a subject suffering from an epilepsy condition, at any point in time the subject may be in a different states along the continuum, such as an ictal state (a state in which a neurological event, such as a seizure, is occurring), a pro-ictal state (a state in which the subject has an increased risk of transitioning to the ictal state), an inter-ictal state (a state in between ictal states), a contra-ictal state (a state in which the subject has a low risk of transitioning to the ictal state within a calculated or predetermined time period), or the like. A pro-ictal state may transition to either an ictal or inter-ictal state.
The estimation and characterization of “state” may be based on one or more subject dependent parameters from a portion of the subject's body, such as electrical signals from the brain, including but not limited to electroencephalogram signals and electrocorticogram signals “ECoG” or intracranial EEG (referred to herein collectively as “EEG”), brain temperature, blood flow in the brain, concentration of AEDs in the brain or blood, changes thereof, etc. While parameters that are extracted from brain-based signals are preferred, the present invention may also extract parameters from other portions of the body, such as the heart rate, respiratory rate, blood pressure, chemical concentrations, etc.
An “event” is used herein to refer to a specific event in the subject's condition. Examples of such events include transition from one state to another state, e.g., an electrographic onset of seizure, end of seizure, or the like. For conditions other than epilepsy, the event could be an onset of a migraine headache, onset of a depressive episode, a tremor, or the like.
The occurrence of a seizure may be referred to as a number of different things. For example, when a seizure occurs, the subject is considered to have exited a “pro-ictal state” and has transitioned into the “ictal state”. However, the electrographic onset of the seizure (one event) and/or the clinical onset of the seizure (another event) have also occurred during the transition of states.
A subject's “susceptibility” for a seizure is a measure of the likelihood of transitioning into the ictal state. The subject's susceptibility for seizure may be estimated by determining which “state” the subject is currently in. As noted above, the subject is deemed to have an increased susceptibility for transitioning into the ictal state (e.g., have a seizure) when the subject is determined to be in a pro-ictal state. Likewise, the subject may be deemed to have a low susceptibility for transitioning into the ictal state when it is determined that the subject is in a contra-ictal state.
While the discussion below focuses on measuring electrical signals generated by electrodes placed near, on, or within the brain or nervous system (EEG signals) of subjects and subject populations for the determination of a subject's susceptibility for having a seizure, it should be appreciated that the invention is not limited to measuring EEG signals or to determining the subject's susceptibility for having a seizure. For example, the invention could also be used in systems that measure one or more of a blood pressure, blood oxygenation indicator e.g. via pulse oximetry, temperature of the brain or of portions of the subject, blood flow measurements, ECG/EKG, heart rate signals, respiratory signals, chemical concentrations of neurotransmitters, chemical concentrations of medications, pH in the blood, or other physiological or biochemical parameters of a subject.
Furthermore, while the remaining discussion focuses on systems and method for measuring a subject's susceptibility for having a seizure, the present invention may also be applicable to monitoring other neurological or psychiatric disorders and determining the susceptibility for such disorders. For example, the present invention may also be applicable to monitoring and management of sleep apnea, Parkinson's disease, essential tremor, Alzheimer's disease, migraine headaches, depression, eating disorders, cardiac arrhythmias, bipolar spectrum disorders, or the like. The present invention may also be applicable to non-medical monitoring and management of events such as storms, earthquakes, social unrest, or other episodic events from which identification of a low susceptibility state may be useful. As can be appreciated, the features extracted from the signals and used by the algorithms will be specific to the underlying disorder that is being managed. While certain features may be relevant to epilepsy, such features may or may not be relevant to the state measurement for other disorders.
The devices and systems of the present invention can be used for long-term, ambulatory sampling and analysis of one or more physiological signals, such as a subject's brain activity (e.g., EEG). In many embodiments, the systems and methods of the present invention incorporate brain activity analysis algorithms that extract one or more features from the brain activity signals (and/or other physiological signals) and classifies, or otherwise processes, such features to determining the subject's susceptibility for having a seizure.
Some systems of the present invention may also be used to facilitate delivery of a therapy to the subject to prevent the onset of a seizure and/or abort or mitigate a seizure. Facilitating the delivery of the therapy may be carried out by outputting a warning or instructions to the subject or automatically initiating delivery of the therapy to the subject (e.g., pharmacological, electrical stimulation, focal cooling, etc.). The therapy may be delivered to the subject using an implanted assembly that is used to collect the ambulatory signals, or it may be delivered to the subject through a different implanted or external assembly.
A description of some systems that may be used to delivery a therapy to the subject are described in commonly owned U.S. Pat. Nos. 6,366,813 and 6,819,956, U.S. Patent Application Publication Nos. 2005/0021103 (published Jan. 27, 2005), 2005/0119703 (published Jun. 2, 2005), 2005/0021104 (published Jan. 27, 2005), 2005/0240242 (published Oct. 27, 2005), 2005/0222626 (published Oct. 6, 2005), and U.S. patent application Ser. No. 11/282,317 (filed Nov. 17, 2005), Ser. Nos. 11/321,897, 11/321,898, and 11/322,150 (all filed Dec. 28, 2005), the complete disclosures of which are incorporated herein by reference.
For subjects suspected or known to have epilepsy, the systems of the present invention may be used to collect data and quantify metrics for the subjects that heretofore have not been accurately measurable. For example, the data may be analyzed to (1) determine whether or not the subject has epilepsy, (2) determine the type of epilepsy, (3) determine the types of seizures, (4) localize or lateralize one or more seizure foci or seizure networks, (5) assess baseline seizure statistics and/or change from the baseline seizure statistics (e.g., seizure count, frequency, duration, seizure pattern, etc.), (6) monitor for sub-clinical seizures, assess a baseline frequency of occurrence, and/or change from the baseline occurrence, (7) measure the efficacy of AED treatments, deep brain or cortical stimulation, peripheral nerve stimulation, and/or cranial nerve stimulation, (8) assess the effect of adjustments of the parameters of the AED treatment, (9) determine the effects of adjustments of the type of AED, (10) determine the effect of, and the adjustment to parameters of, electrical stimulation (e.g., peripheral nerve stimulation, cranial nerve stimulation, deep brain stimulation (DBS), cortical stimulation, etc.), (11) determine the effect of, and the adjustment of parameters of focal cooling (e.g., use of cooling fluids, peltier devices, etc., to diminish or reduce seizures (see, for example, “Rothman et al., “Local Cooling: A Therapy for Intractable Neocortical Epilepsy,” Epilepsy Currents, Vol. 3, No. 5, September/October 2003; pp. 153-156, (12) determine “triggers” for the subject's seizures, (13) assess outcomes from surgical procedures, (14) provide immediate biofeedback to the subject, (15) screen subjects for determining if they are an appropriate candidate for a seizure advisory system or other neurological monitoring or therapy system, or the like.
In a first aspect of the invention, the present invention encompasses a data collection system that is adapted to collect long term ambulatory brain activity data from the subject. In preferred embodiments, the data collection system is able to sample one or more channels of brain activity from the subject with one or more implanted electrodes. The electrodes are in wired or wireless communication with one or more implantable assemblies that are, in turn, in wired or wireless communication with an external assembly. The sampled brain activity data may be stored in a memory of the implanted assembly, external assembly and/or a remote location such as a physician's computer system. In alternative embodiments, it may be desirable to integrate the electrodes with the implanted assembly, and such an integrated implanted assembly may be in communication with the external assembly.
Unlike other conventional systems which have an implanted memory that is able to only store small epochs of brain activity before and after a seizure, the implantable assemblies of the present invention are configured to substantially continuously sample the physiological signals over a much longer time period (e.g., anywhere between one day to one week, one week to two weeks, two weeks to a month, or more) so as to be able to monitor fluctuations of the brain activity (or other physiological signal) over the entire time period. In alternative embodiments, however, the implantable assembly may only periodically sample the subject's physiological signals or selectively/aperiodically monitor the subject's physiological signals. Some examples of such alternative embodiments are described in commonly owned U.S. patent application Ser. Nos. 11/616,788 and 11/616,793, both filed Dec. 27, 2006, the complete disclosures of which are incorporated herein by reference.
When the memory is almost full, the system may provide the subject a warning so that the subject may manually initiate uploading of the collected brain activity data or the system may automatically initiate a periodic download of the collected brain activity data from a memory of the external assembly to a hard drive, flash-drive, local computer workstation, remote server or computer workstation, or other larger capacity memory system. In alternative embodiments, the external assembly may be configured to automatically stream the stored EEG data over a wireless link to a remote server or database. Such a wireless link may use existing WiFi networks, cellular networks, pager networks or other wireless network communication protocols. Advantageously, such embodiments would not require the subject to manually upload the data and could reduce the down time of the system and better ensure permanent capture of substantially all of the sampled data.
Another aspect of the invention is a system for monitoring a subject's susceptibility, or susceptibility, to a seizure. The system includes an electrode and an implanted communication assembly in communication with the electrode. The implanted communication assembly samples a neural signal with the electrode and substantially continuously transmits a data signal from the subject's body. The system also comprises an external assembly positioned outside the subject's body that is configured to receive and process the data signal to measure the subject's susceptibility to having a seizure. In alternative embodiments the implanted assembly processes the data and measures the subject's susceptibility of having a seizure, in which case only data indicative of the measured susceptibility is transmitted to the external assembly.
The electrode arrays 12 of the present invention may be intracranial electrodes (e.g., epidural, subdural, and/or depth electrodes), extracranial electrodes (e.g., spike or bone screw electrodes, subcutaneous electrodes, scalp electrodes, dense array electrodes), or a combination thereof. While it is preferred to monitor signals directly from the brain, it may also be desirable to monitor brain activity using sphlenoidal electrodes, foramen ovale electrodes, intravascular electrodes, peripheral nerve electrodes, cranial nerve electrodes, or the like. While the remaining disclosure focuses on intracranial electrodes for sampling intracranial EEG, it should be appreciated that the present invention encompasses any type of electrodes that may be used to sample any type of physiological signal from the subject.
In the configuration illustrated in
Some exemplary configurations of the electrode arrays 12 are shown in
If the system 10 includes the capability of providing stimulation of the peripheral nerve, such as the vagus nerve, the system may include a vagus nerve cuff 36, which includes a modified IS1 connector that is used for Cyberonics vagus nerve lead. The systems 10 of the present invention may also be configured to provide electrical stimulation to other portions of the nervous system (e.g., cortex, deep brain structures, cranial nerves, etc.). Stimulation parameters are typically about several volts in amplitude, 50 microsec to 1 milisec in pulse duration, and at a frequency between about 2 Hz and about 1000 Hz.
As shown in
Implanted assembly 14 can be used to pre-process EEG signals sampled by the electrode array 12 and transmit a data signal that is encoded with the sampled EEG data over a wireless link 18 to an external assembly 20, where the EEG data is permanently or temporarily stored.
Packaging 40 is preferably as small as possible and may have a similar packaging footprint as a spinal cord stimulator. Thus, the packaging typically has a volume between about 10 cubic centimeters to about 70 cubic centimeters and preferably about 30 cubic centimeters, but may be larger or smaller, depending on what components are disposed therein. Packaging 40 comprises an interface 41 for the connectors 19 of leads 16. The interface 41 will have at least the same number of input channels as the number of contacts in the electrode array, and may have more input channels than active contacts. Interface 41 may also have one or more bipolar output channels for delivering electrical stimulation to a peripheral nerve, brain tissue, cranial nerves, or other portions of the subject's body. Further details of an exemplary housing structure for the implanted assembly can be found in U.S. Application No. 61/017,504, filed Dec. 28, 2007, the disclosure of which is incorporated by reference herein in its entirety.
The interconnections between the components of implanted assembly 14 and external assembly 20 may be may be wired, wireless, digital, analog, or any combination thereof, and such electronic components may be embodied as hardware, software, firmware, or any combination thereof. While
The electronic components of the implanted assembly will typically comprise a signal conditioning sub-assembly 42 that conditions the one or more EEG signals received from the interface 41. The signal conditioning sub-assembly 42 may perform amplification, combined to reduce common mode signal, filtering (e.g., lowpass, highpass, bandpass, and/or notch filtering), digital-to-analog conversion, or some combination thereof.
The electronic components of the implanted assembly 14 may optionally comprise dedicated circuitry and/or a microprocessor (referred to herein collectively as “processing sub-assembly 44”) for further processing of the EEG signals prior to transmission to the external assembly 20. The further processing may include any combination of encryption, forward error correction, checksum or cyclic redundancy checks (CRC), or the like. The processing sub-assembly may comprise an ASIC, off the shelf components, or the like. In one embodiment processing sub-assembly 44 includes one or more multiple-core processors for processing data. Such multiple-core microprocessors provide faster processing, while consuming less power than multiple single core processors. Consequently, the life of the power source 44 may be prolonged. Some examples of suitable multiple-core processors include the Intel® Core 2 Duo Processor and the AMD® dual-core Opteron microprocessor.
Of course, while
The implanted assembly 14 will also typically include both a clock 48 and a power source 50. The clock 48 is typically in the form of an oscillator and frequency synthesizer to provide synchronization and a time base for the signals transmitted from internal assembly and for signals received from external assembly 20. Power source 50 may be a non-rechargeable battery, a rechargeable battery, a capacitor, etc. One preferred power source is a medical grade rechargeable Li-Ion battery that is commonly used in other implantable devices. The rechargeable power source 50 may also be in communication with the communication sub-system 46 so as to receive power from outside the body by inductive coupling, radiofrequency (RF) coupling, etc. Such rechargeable power sources typically have a lifespan of between about 3 years and about 5 years. Power source 50 will generally be used to provide power to the other components of the implantable assembly 14.
In some embodiments, the implanted assembly 14 may optionally include a memory sub-system 52 (e.g., RAM) for permanently or temporarily storing or buffering the processed EEG signal. For example, memory sub-assembly 52 may be used as a buffer to temporarily store the processed EEG data if there are problems with transmitting the data to the external assembly. For example, if the external assembly's power supply is low, the memory in the external assembly is removed, or if the external assembly is out of communication range with the implantable assembly 14, the EEG data may be temporarily buffered in memory sub-assembly 52 and the buffered EEG data and the current sampled EEG data may be transmitted to the external assembly when the problem has been corrected. The buffer may be any size, but it will typically be large enough to store between about 1 megabyte and 100 megabytes of data. As can be appreciated, as technology improves and the capacity of memory cards improve, it is likely that many hundreds of gigabytes or hundreds of gigabytes of data may be buffered in the internal memory. Of course, in embodiments that do not have a memory sub-system 52 in the implanted assembly 14, any data that is sampled during the times in which the external assembly 20 is out of communication range with the implanted assembly 14, there may simply be gaps in the stored data.
In some embodiments the system 10 of the present invention may incorporate an alert that is activated to indicate that there is a communication error between the implanted assembly and the external assembly. Exemplary communication errors include, without limitation, when (1) the external assembly 20 is out of communication range with the internal assembly 14 such that the transmitted data signals are not received by the external assembly, (2) there is some other error in the transmission and receipt of data signals between the internal assembly 14 and external assembly, (3) self test error has been encountered, (4) memory card is full (or nearly full), or some combination thereof. Additional exemplary causes for an alert are discussed below in the more detailed discussion of the external assembly.
Typically, the alert is incorporated in the external assembly 20 so that the external assembly can provide a visual, audible, and/or tactile alert. Such an alert can indicate to the subject (or third party) that the external assembly 20 is not able to receive the RF signal from the implanted assembly 14 and/or that appropriate data transfer is not occurring. Moreover, the alert may reduce the likelihood of misplacing the external assembly 20, since in most embodiments, once the data transfer is interrupted, the alert may be activated by the system. In such a case, if the subject were to walk away from the external assembly 20 (e.g., leave the external assembly 20 on a table), the subject would not be advised of their susceptibility for seizure. If the subject did not realize that they did not have their external assembly 20 with them, the subject may assume that they are in a low susceptibility and perform activities on the assumption that their external assembly 20 would warn them of a changing to a state in which they were in a higher susceptibility to a seizure.
Additionally or alternatively, it may be possible to incorporate an alert in the implanted assembly 14 and the alert may provide a tactile warning (e.g., vibration) and/or audible alert to warn the subject that there is a data transmission error between the external assembly 20 and the implanted assembly 14.
In some embodiments the external assembly can be adapted so that it will expect to receive a data signal from the implanted assembly, and if it does not, the alert will be activated. The external assembly can be programmed to expect to receive a substantially continuous data signal from the implanted assembly, such that if the external assembly stops receiving a signal the alert will be activated. The external assembly can also be programmed to expect to receive a data signal periodically rather than substantially continuously. For example, the external assembly could expect to receive a signal every two seconds, and if it fails to receive a signal after a two second period of time, the alert will be activated. Thus, when the external assembly is adapted to expect a data signal periodically, the alert will be activated after a specified period of time passes without the external assembly receiving the data signal.
In some embodiments the communication error comprises a gap in the communication stream. For example, if the data signal comprises a numbered sequence of packets of information, and the external assembly receives a signal with missing packets of information within the sequence, the alert would be activated. The implanted assembly can be adapted to temporarily store the data signal so that if the external assembly detects a gap in the communication, the implanted assembly can attempt to retransmit the complete data signal data.
In some embodiments the communication error can include data formatting errors. An exemplary formatting error is an invalid cyclic redundancy check, but formatting errors as described herein include any other alteration of data during transmission or storage.
In some situations, the subject may be able to temporarily disable the alert and/or change the mode or parameters of the alert using a subject input. Such functionality may be carried out through providing a manual subject input—such as pressing a button on the external assembly 20.
In some embodiments, external assembly 20 may be programmed to allow the subject to disable the alert if the subject is in one or more different neurological states. For example, if the subject is in a contra-ictal state in which the subject is at a low susceptibility to transitioning into an ictal state and/or a pro-ictal state in a period of time and did not want to carry the external assembly 20 with them (e.g., to take a shower and leave the external assembly 20 in the bedroom), the subject may disable the alert by using the buttons 131, 133, 135 or other user inputs on the external assembly 20 (
The subject and/or the physician may also customize the alert parameters to the subject. For example, some subjects may want to be immediately alerted if there is a communication error, while others may want a time delay before the alert is sounded.
Furthermore, if there is a prolonged alert (e.g., the subject leaves the house without the external assembly), the external assembly 20 may automatically disable the alert after a predetermined time and/or the alert may be manually disabled by a third party. To further reduce the likelihood of misplacing the external assembly 20 and ensuring that the subject is being monitored and advised, the external assembly 20 may comprise a communication assembly that facilitates the wireless communication with a remote party, such as the subject's caregiver, spouse, or friend (described in more detail below as the caregiver advisory device). Thus, if an alert is sounded that indicates a communication error, the communication assembly may send a wireless communication to the remote party to alert the third party that the subject is not being advised of their susceptibility to seizure. Typically, the wireless communication to the caregiver will be sent only after a predetermined time period has elapsed.
Tuning or reprogramming of the components of implanted assembly 14 may be carried out in vivo through communication sub-assembly 46. For example, the external assembly 20 and/or a dedicated programmer (controlled by physician) may be brought into communication range with the communication sub-assembly 46 and the reprogramming instructions may be uploaded into the processing sub-assembly.
Communication sub-assembly may include a magnetic reed switch (not shown) similar to those found in the Cyberonics® Vagus Nerve Stimulator or spinal cord stimulators. The magnetic reed switch would enable initiation of an electrode impedance check, self test, RAM check, ROM check, power supply checks, computer operating properly checks, electrode impedance check, or the like.
Implantable assembly 14 can be configured to substantially continuously sample the brain activity of the groups of neurons in the immediate vicinity of each of the contacts in the electrode array 12. The communication range between the implanted assembly 14 and the external assembly 20 is typically about 5 meters, but could be as short as requiring that the external assembly 20 contact the skin of the subject and up to 10 meters, or more. Sampling of the brain activity is typically carried out at a sampling rate above about 200 Hz, and preferably between about 200 Hz and about 1000 Hz, and most preferably between about 400 Hz and about 512 Hz, but it could be higher or lower, depending on the specific condition being monitored, the subject, and other factors. Each sample of the subject's brain activity will typically contain between about 8 bits per sample and about 32 bits per sample, and preferably between about 12 bits and 16 bits per sample. The wireless communication link 18 may have an overall data transfer rate between approximately 5 Kbits/sec and approximately 500 Kbits/sec, and preferably about approximately 50 kbits/sec. As can be appreciated, the over air data transfer rate of the implanted assembly could be considerably higher (e.g., 2 Mbits/sec), which would allow for a lower transmit duty cycle which will result in power savings.
For example, if each communication transmission to the external assembly includes one EEG sample per transmission, and the sample rate is 400 Hz and there are 16 bits/sample, the data transfer rate from the implantable assembly 14 to the external assembly 20 is at least about 6.4 Kbits/second/channel. If there are 16 channels, the total data transfer rate for the wireless communication link 18 between the implanted assembly 14 and the external assembly 20 would be about 102 Kbits/second.
While substantially continuous sampling and transmission of brain activity is preferred, in alternative embodiments, it may be desirable to have the implantable assembly 14 sample the brain activity of the subject in a non-continuous basis or the sampling rate may vary over the period of monitoring. In such embodiments, the implantable assembly 14 may be configured to sample the brain activity signals periodically (e.g., a burst of sampling every 5 seconds) or aperiodically. For example, it may be desirable to reduce or increase the sampling rate when a subject has gone to sleep.
To enable the high data transfer rates of the present invention, the wireless communication link 18 provided by the communication sub-assembly 46 is typically in the form of an electromagnetic radiofrequency communication link. Conventional devices typically use a slower communication link (e.g., that is designed for low data transfer rates and long link access delays) and transmit data out on a non-continuous basis. In contrast, the present invention uses a fast access communication link that transmits smaller bursts of data (e.g., single or small number of EEG samples from each of the channels at a time) on a substantially continuous basis so as to allow for substantially real-time analysis of the EEG data. The radiofrequency used to transfer data between the implantable assembly 14 and external assembly 20 is at a frequency typically between 13.56 MHz and 10 GHz, preferably between about 900 MHz and about 2.4 GHz, more preferably at about 2.4 GHz, or between about 900 MHz and about 928 MHz. One potentially useful communication sub-assembly is a 900 MHz ISM telemetry transmitter. If it is desired to avoid FCC regulations, it may be desirable to use telemetry at low frequency, such as below 9 Khz.
As can be appreciated, while the aforementioned frequencies are the preferred frequencies, the present invention is not limited to such frequencies and other frequencies that are higher and lower may also be used. For example, it may be desirable us use the MICS (Medical Implant Communication Service band) that is between 402-405 MHz to facilitate the communication link.
In order to facilitate data transmission from the implanted assembly 14 to the external assembly 20, the antennas 47 and 62 of the implantable assembly 14 and external assembly 14, respectively, must be maintained in communication range of each other. The frequency used for the wireless communication link has a direct bearing on the communication range. Typically, the communication range is typically at least one foot, preferably between about one foot and about twenty feet, and more preferably between about six feet and sixteen feet. As can be appreciated, however, the present invention is not limited to such communication ranges, and larger or smaller communication ranges may be used. For example, if an inductive communication link is used, the communication range will be smaller than the aforementioned range; but if higher frequencies are used, the communication range may be larger than twenty feet.
While not illustrated in
In some situations, instead of a wireless link between the implanted assembly 14 and the external assembly 20, it may be desirable to have a wire running from the subject-worn data collection assembly 20 to an interface (not shown) that could directly link up to the implanted assembly 14 that is positioned below the subject's skin. For example, the interface may take the form of a magnetically attached transducer, as with cochlear implants. This could enable higher rates of data transmission between the implanted assembly 14 and the external assembly 20.
The illustrated external assembly shows a user interface 72 that includes a variety of indicators for providing system status and alerts to the subject. User interface 72 may include one or more indicators 101 that indicate the subject's brain state. In the illustrated embodiment, the output includes light indicators 101 (for example, LEDs) that comprise one or more (e.g., preferably two or more) discrete outputs that differentiate between a variety of different brain states. In the illustrated embodiment, the brain state indicators 101 include a red light 103, yellow/blue light 105, and a green light 107 for indicating the subject's different brain states (described more fully below). In some configurations the lights may be solid, blink or provide different sequences of flashing to indicate different brain states. If desired, the light indicators may also include an “alert” or “information” light 109 that is separate from the brain state indicators so as to minimize the potential confusion by the subject.
External assembly 20 may also include a liquid crystal display (“LCD”) 111 or other display for providing system status outputs to the subject. The LCD 111 generally displays the system components' status and prompts for the subject. For example, as shown in
Similar to the other embodiments, the external assembly of
The LCD 111 and brain state indicators 101 are typically viewable by the subject when it is attached to the subject's belt. As such, the subject need only glance down onto the top surface of the PAD when an audible or tactile indication is provided that indicates a subject's brain state or change thereof.
In the embodiment of
The front surface of the external assembly may also comprise a door 9 that houses the removable data card an on/off input button (not shown). When opened, the subject may replace the full (or defective) data card with a new card. Alternatively, if the subject desires to turn on or off the external assembly, the subject may activate the on/off input. Typically, the subject will keep the external assembly on at all times, but in instances which require the external assembly to be off (e.g., on an airplane), the subject may have the ability to turn off the external assembly and stop the transmission of the data signal from the implanted device—which may help to conserve battery power of the external assembly and implanted device.
Referring again to
The external assembly 20 preferably comprises one or more subject inputs that allow the subject to provide inputs to the external assembly. In the illustrated embodiment, the inputs comprise one or more physical inputs (e.g., buttons 131, 133, 135) and an audio input (in the form of a microphone 137 and a pre-amp circuit).
Similar to conventional cellular phones, the inputs 131, 133, 135 may be used to toggle between the different types of outputs provided by the external assembly. For example, the subject can use buttons 133 to choose to be notified by tactile alerts such as vibration rather than audio alerts (if, for example, a subject is in a movie theater). Or the subject may wish to turn the alerts off altogether (if, for example, the subject is going to sleep). In addition to choosing the type of alert, the subject can choose the characteristics of the type of alert. For example, the subject can set the audio tone alerts to a low volume, medium volume, or to a high volume.
Some embodiments of the external assembly 20 will allow for recording audio, such as voice data. A dedicated voice recording user input 131 may be activated to allow for voice recording. In preferred embodiments, the voice recording may be used as an audio subject seizure diary. Such a diary may be used by the subject to record when a seizure has occurred, when an aura or prodrome has occurred, when a medication has been taken, to record subject's sleep state, stress level, etc. Such voice recordings may be time stamped and stored in data storage of the external assembly and may be transferred along with recorded EEG signals to the physician's computer. Such voice recordings may thereafter be overlaid over the EEG signals and used to interpret the subject's EEG signals and improve the training of the subject's customized algorithm, if desired.
The one or more inputs may also be used to acknowledge system status alerts and/or brain state alerts. For example, if the external assembly provides an output that indicates a change in brain state, one or more of the LEDs 101 may blink, the vibratory output may be produced, and/or an audio alert may be generated. In order to turn off the audio alert, turn off the vibratory alert and/or to stop the LEDs from blinking, the subject may be required to acknowledge receiving the alert by actuating one of the user inputs (e.g., button 135).
While the external assembly is shown having inputs 131, 133, 135, any number of inputs may be provided on the external assembly. For example, in one alternate embodiment, the external assembly may comprise only two input buttons. The first input button may be a universal button that may be used to scroll through output mode options. A second input button may be dedicated to voice recording. When an alert is generated by the external assembly, either of the two buttons may be used to acknowledge and deactivate the alert. In other embodiments, however, there may be a dedicated user input for acknowledging the alerts.
External assembly 20 may comprise a main processor 139 and a complex programmable logic device (CPLD) 141 that control much of the functionality of the external assembly. In the illustrated configuration, the main processor and/or CPLD 141 control the outputs displayed on the LCD 111, generates the control signals delivered to the vibration device 127 and speaker 125, and receives and processes the signals from buttons 131, 133, 135, microphone 137, and a real-time clock 149. The real-time clock 149 may generate the timing signals that are used with the various components of the system.
The main processor may also manage a data storage device 151, provides redundancy for a digital signal processor 143 (“DSP”), and manage the telemetry circuit 147 and a charge circuit 153 for a power source, such as a battery 155.
While main processor 139 is illustrated as a single processor, the main processor may comprise a plurality of separate microprocessors, application specific integrated circuits (ASIC), or the like. Furthermore, one or more of the microprocessors 139 may include multiple cores for concurrently processing a plurality of data streams.
The CPLD 141 may act as a watchdog to the main processor 139 and the DSP 143 and may flash the LCD 111 and brain state indicators 101 if an error is detected in the DSP 143 or main processor 139. Finally, the CPLD 141 controls the reset lines for the main microprocessor 139 and DSP 143.
A telemetry circuit 147 and antenna may be disposed in the PAD 10 to facilitate one-way or two-way data communication with the implanted device. The telemetry circuit 147 may be an off the shelf circuit or a custom manufactured circuit. Data signals received from the implanted device by the telemetry circuit 147 may thereafter be transmitted to at least one of the DSP 143 and the main processor 139 for further processing.
The DSP 143 and DRAM 145 receive the incoming data stream from the telemetry circuit 147 and/or the incoming data stream from the main processor 139. The brain state algorithms process the data (for example, EEG data) and estimate the subject's brain state, and are preferably executed by the DSP 143 in the PAD. In other embodiments, however, the brain state algorithms may be implemented in the implanted device, and the DSP may be used to generate the communication to the subject based on the data signal from the algorithms in the implanted device.
The main processor 139 is also in communication with the data storage device 151. The data storage device 151 preferably has at least about 7 GB of memory so as to be able to store data from about 8 channels at a sampling rate of between about 200 Hz and about 1000 Hz. With such parameters, it is estimated that the 7 GB of memory will be able to store at least about 1 week of subject data. Of course, as the parameters (e.g., number of channels, sampling rate, etc.) of the data monitoring change, so will the length of recording that may be achieved by the data storage device 151. Furthermore, as memory capacity increases, it is contemplated that the data storage device will be larger (e.g., 10 GB or more, 20 GB or more, 50 GB or more, 100 GB or more, etc.). Examples of some useful types of data storage device include a removable secure digital card or a USB flash key, preferably with a secure data format.
“Subject data” may include one or more of raw analog or digital EEG signals, compressed and/or encrypted EEG signals or other physiological signals, extracted features from the signals, classification outputs from the algorithms, etc. The data storage device 151 can be removed when full and read in card reader 157 associated with the subject's computer and/or the physician's computer. If the data card is full, (1) the subsequent data may overwrite the earliest stored data or (2) the subsequent data may be processed by the DSP 143 to estimate the subject's brain state (but not stored on the data card). While preferred embodiments of the data storage device 151 are removable, other embodiments of the data storage device may comprise a non-removable memory, such as FLASH memory, a hard drive, a microdrive, or other conventional or proprietary memory technology. Data retrieval off of such data storage devices 151 may be carried out through conventional wired or wireless transfer methods.
The power source used by the external assembly may comprise any type of conventional or proprietary power source, such as a non-rechargeable or rechargeable battery 155. If a rechargeable battery is used, the battery is typically a medical grade battery of chemistries such as a lithium polymer (LiPo), lithium ion (Li-Ion), or the like. The rechargeable battery 155 will be used to provide the power to the various components of the external assembly through a power bus (not shown). The main processor 139 may be configured to control the charge circuit 153 that controls recharging of the battery 155.
In addition to being able to communicate with the implanted device, the external assembly may have the ability to communicate wirelessly with a remote device—such as a server, database, physician's computer, manufacturer's computer, or a caregiver advisory device (all of which can be herein referred to as “CAD”). In the exemplary embodiment, the external assembly may comprise a communication assembly (not shown) in communication with the main processor 139 that facilitates the wireless communication with the remote device. The communication assembly may be a conventional component that is able to access a wireless cellular network, pager network, wifi network, or the like, so as to be able to communicate with the remote device. The wireless signal could be transfer of data, an instant message, an email, a phone call, or the like.
In one particular embodiment, the external assembly is able to deliver a signal through the communication assembly that is received by the CAD so as to inform the caregiver of the subject's brain state or change in brain state. The CAD would allow the caregiver to be away from the subject (and give the subject independence), while still allowing the caregiver to monitor the subject's brain state and susceptibility for seizure. Thus, if the subject's brain state indicates a high susceptibility for a seizure or the occurrence of a seizure, the caregiver would be notified via the CAD, and the caregiver could facilitate an appropriate treatment to the subject (e.g., small dosage of an antiepileptic drug, make the subject safe, etc.). A signal may be provided to the caregiver only if the subject has a high susceptibility for a seizure or if a seizure is detected, or it may provide the same indications that are provided to the subject.
In yet other embodiments, the communication assembly could be used to inform the caregiver that there is a communication error between the subject's implanted assembly and external assembly, so as to indicate that the subject is not being properly monitored and advised. Such a communication would allow the caregiver to intervene and/or inform the subject that they are not being monitored.
In other embodiments, the communication assembly could be used to facilitate either real-time or non-real time data transfer to the remote server or database. If there is real time transfer of data, such a configuration could allow for remote monitoring of the subject's brain state and/or EEG signals. Non-real time transfer of data could expedite transfer and analysis of the subject's recorded EEG data, extracted features, or the like. Thus, instead of waiting to upload the brain activity data from the subject's data storage device, when the subject visits their physician, the physician may have already had the opportunity to review and analyze the subject's transferred brain activity data prior to the subject's visit.
The external assembly may be configured to perform a self hardware/software test to detect system errors—such as power failures, software failures, impedance change, battery health of the implanted device and external assembly, internal clock and voltage reference, hardware (processors, memory, and firmware) checks, or the like. The self test may be performed periodically, upon initial startup, upon a system reset, or some combination thereof. The system preferably runs a self-test on the external assembly, implanted device, electrode array and the communication links. The external assembly may emit a tone and/or display information on the LCD at the initiation of the self-test(s). If the external assembly, implanted device, electrode array and/or communication link pass the self-test, the subject may be notified with an alert indicating the respective devices passed the self-test. If any of the components do not pass the self-test, the subject can be alerted with an output that indicates which component did not pass (for example, an icon on the LCD representing the component which did not pass the test flashes). There may also be an audio alert, such as a voice alert, that one or some of the devices failed the test. The external assembly may also indicate these failures with information or alert light 109 (
The external assembly may be configured to be toggled between two or more different modes of operation. In one embodiment, the physician may toggle the external assembly between three different modes of operations. Of course, it should be appreciated that the external assembly may have as little as one mode of operation, or more than three different modes of operations.
In one example, a first mode of operation of the external assembly may be merely data collection, in which data signals from the implanted device are stored in the data storage 151 of the external assembly. In such a mode, the user interface 72 may be modified to only provide system status indications to the subject via the LCD 111, and the brain state indicators 101 may be temporarily disabled.
In a second mode of operation, after the brain state algorithms have been trained on the subject's data that was collected during the first mode of operation, the brain state algorithms may be implemented to process substantially real-time data signals and the brain state indicators 101 may be enabled so as to inform the subject of their substantially real-time brain state.
In a third mode of operation, it may be desirable to only receive and process the data signals from the implanted device, but no longer store the substantially continuous data signals in a memory of the external assembly. For example, if the brain state algorithms are performing as desired, the brain data signals from the implanted device will not have to be stored and analyzed. Consequently, the subject would not have to periodically replace the data card as frequently. However, it may still be desirable to store the data signals that immediately precede and follow any detected seizure. Consequently, in the third mode such seizure data signals may optionally be stored.
As noted above, in some embodiments the system comprises one or more brain state algorithms. In one embodiment, the brain state algorithms embodied in the present invention will generally characterize the subject's brain state as either “Low Susceptibility,” “Unknown,” “Elevated Susceptibility” or “Detection.” It is intended that these are meant to be exemplary categories and are in no way to be limiting and additional brain states or fewer brain state indicators may be provided. There may be different types of algorithms which are configured to characterize the brain state into more or less discrete states. “Contra-ictal” generally means that brain activity indicates that the subject has a low susceptibility to transition to an ictal state and/or a pro-ictal state for an upcoming period of time (for example, 60 minutes to 90 minutes). This is considered positive information and no user lifestyle action is required. A pro-ictal state generally means that the algorithm(s) in the PAD are determining that the subject has an elevated susceptibility for a seizure (possibly within a specified time period). A “detection” state generally means that brain activity indicates that the subject has already transitioned into an ictal state (e.g., occurrence of an electrographic seizure) or that there is an imminent clinical seizure. User actions should be focused on safety and comfort. An “unknown” state generally means the current type of brain activity being monitored does not fit within the known boundaries of the algorithms and/or that the brain activity does not fit within the contra-ictal state, pro-ictal state, or ictal state. Therefore no evaluation can be reliably made. “Unknown” can also indicate there has been a change in the status of the brain activity and while the subject does not have an elevated susceptibility and no seizure has been detected, it is not possible to reliable tell the subject that they may not transition into an ictal state and/or pro-ictal state for a period of time. This state is considered cautionary and requires some cautionary action such as limiting exposure to risk. The two different types of “unknown” may have separate brain state indicators, or they may be combined into a single brain state indicator, or the user interface may not provide the “unknown” state to the subject at all.
The external assembly preferably comprises visual indicators, such as LEDs, notifying the subject of the determined brain state. In one preferred embodiments, the visual indicators for the brain state alerts will comprise a green, yellow/blue, and red lights. The green light will be illuminated when the PAD determines that the brain state is in a “low susceptibility to seizure” state. The yellow or blue light will be illuminated when the subject is in an “unknown” state. The PAD will emit a solid red light when the subject is in the “high susceptibility” state. The PAD will emit a blinking red light when the subject is in the “detection” state. The light colors or number of light indicators are not intended to be limiting. Any color may be used. It may be desirable to include additional lights or colors (e.g., orange) to further delineate the subject's estimated condition. In yet other embodiments, it may be desirable to display only a green light and red light.
Further exemplary details of external assembly 20 can be found in U.S. Provisional Application No. 60/952,463, filed Jul. 27, 2007, the disclosure of which is incorporated by reference herein in its entirety.
In preferred embodiments, the wireless signal is transmitted substantially immediately after sampling of the EEG signal to allow for substantially continuous real-time transfer of the subject's EEG data to the external assembly 20. In alternate embodiments, however, the RF signal with the encoded EEG data may be temporarily buffered in an internal memory 52 (
Instead of sending large packets of stored data with each RF communication transmission, the methods and devices of the present invention substantially continuously sample physiological signals from the subject and communicate in real-time small bits of data during each RF signal communication to the external assembly. Of course, for embodiments in which real-time data transfer is not needed, it may be desirable to transmit larger packets of data to the external assembly 20 using the communication link, and such a communication protocol is also encompassed by the present invention.
As noted above, the data signals that are wirelessly transmitted from implanted assembly 14 may be encrypted so as to help ensure the privacy of the subject's data prior to transmission to the external assembly 20. Alternatively, the data signals may be transmitted to the external assembly 20 with unencrypted EEG data, and the EEG data may be encrypted prior to the storage of the EEG data in the memory of external assembly 20 or prior to transfer of the stored EEG data to the local computer workstation 22 or remote server 26.
The download of brain activity data may be manually carried out by the subject or automatically initiated by a component of system 10. After a time period of collecting EEG data (e.g., one day to one week, one week to two weeks, two weeks to one month, etc.), the external assembly 20 may be manually put in communication with a local computer workstation 22 through either a wireless link or wired link to download the stored data to a memory of the local computer workstation 22. For example, in one embodiment, a wired USB 2.0 connection (improvements thereof or other conventional interface) may be used to upload the stored EEG data to the local computer workstation 22. Alternatively, instead of downloading the data directly to a local or remote computer workstation 22, 26, the data may be downloaded to a portable hard drive or flash drive for temporary storage. In such embodiments, the drive may thereafter be brought or delivered into the physician's office for download and analysis.
Furthermore, as shown in
For example, the local computer workstation 22 (or remote computer workstation 26) may periodically command the external assembly to upload the data from the memory of the external assembly, or the external assembly may be programmed to automatically upload the EEG data according to a predetermined schedule or upon reaching a threshold level memory usage. By incrementally downloading days or weeks of stored brain activity data periodically, the subject's physician may be able to start analysis of the brain activity data and possibly complete the analysis of the long term data prior to the subject going to the physician's office. If a subject were to bring in a week or month of stored brain activity data for analysis by the physician, the subject would have to wait hours, days or even weeks for the analysis of the data to be completed. Consequently, instead of waiting for the analysis, analysis of the data may be substantially completed and therapy or diagnosis decisions may be made prior to the office visit and the subject would be able to immediately implement any changes or start therapy immediately after visiting the office.
Once implanted in the subject, the systems 10 of the present invention may be used for a variety of different data collection and monitoring purposes. For example, in one usage the systems of the present invention may be used to quantify seizure activity statistics for the subject. Currently, the most common method of quantifying a subject's seizure activity is through subject self reporting using a seizure diary. However, it has been estimated that up to 63% of all seizures are missed by subjects. Subject's missing the seizures are usually caused by the subjects being amnesic to the seizures, unaware of the seizures, mentally incapacitated, the seizures occur during sleep, or the like.
Seizure count over a time period—How many clinical and sub-clinical seizures does the subject have in a specific time period?
Seizure frequency—How frequent does the subject have seizures? What is the seizure frequency without medication and with medication? Without electrical stimulation and with electrical stimulation?
Seizure duration—How long do the seizures last? Without medication and with medication? Without electrical stimulation and with electrical stimulation?
Seizure timing—When did the subject have the seizure? Do the seizures occur more frequently at certain times of the day?
Seizure patterns—Is there a pattern to the subject's seizures? After certain activities are performed? What activities appear to trigger seizures for this particular subject?
Finally, at step 102, report generation software may be used to generate a report based on the statistics for the seizure activity. The report may include some or all of the statistics described above, and may also include the EEG signal(s) associated with one or more of the seizures. The report may include text, graphs, charts, images, or a combination thereof so as to present the information to the physician and/or subject in an actionable format.
As noted above, the present invention enables the quantification, documentation and long term monitoring of sub-clinical seizures in a subject. Because the subject is unaware of the occurrence of sub-clinical seizures, heretofore the long term monitoring of sub-clinical seizures was not possible. Documentation of the sub-clinical seizures may further provide insight into the relationship between sub-clinical seizures and clinical seizures, may provide important additional information relevant to the effectiveness of subject therapy, and may further enhance the development of additional treatments for epilepsy.
By way of example, a medically refractory subject coming to an epilepsy center for the first time might first have the system of the present invention implanted and then asked to collect data for a prescribed time period, e.g., 30 days. The initial 30 days could be used to establish a baseline measurement for future reference. The physician could then prescribe an adjustment to the subject's medications and have the subject collect data for another time period, e.g., an additional 30 day period. Metrics from this analysis could then be compared to the previous analysis to see if the adjustment to the medications resulted in an improvement. If the improvement was not satisfactory, the subject can be taken off of the unsatisfactory medication, and a new medication could be tried. This process could continue until a satisfactory level of seizure control was achieved. The present invention provides a metric that allows physicians and subjects to make informed decisions on the effectiveness and non-effectiveness of the medications.
At step 116, the therapy that is to be evaluated is commenced. The therapy will typically be an AED and the subject will typically have instructions from the neurologist, epileptologist, or drug-manufacturer regarding the treatment regimen for the AED. The treatment regimen may be constant (e.g., one pill a day) throughout the evaluation period, or the treatment regimen may call for varying of some parameter of the therapy (e.g., three pills a day for the first week, two pills a day for the second week, one pill a day for the third week, etc.) during the evaluation period. During the evaluation period, the implantable assembly and external assembly will be used to substantially continuously sample the subject's EEG. The sampled EEG may thereafter be processed to obtain a follow-up measurement for the subject (Step 118). If the baseline measurement was seizure statistics for the baseline time period, then the follow-up measurement will be the corresponding seizure statistics for the evaluation period. At step 120, the baseline measurement is compared to the follow-up measurement to evaluate the therapy. If the comparison indicates that the therapy did not significantly change the subject's baseline, the therapy may be stopped, and other therapies may be tried.
Currently, the primary metric in evaluating the efficacy of an AED is whether or not the AED reduces the subject's seizure count. In addition to seizure count, the systems of the present invention would be able to track any reduction in seizure duration, modification in seizure patterns, reduction in seizure frequency, or the like. While seizure count is important, because the present invention is able to provide much greater detail than just seizure count, efficacy of an AED may be measured using a combination of additional metrics, if desired. For example, if the subject was having a large number of sub-clinical seizures, spike bursts, or other epileptiform activity (which the subject was not aware of) and the AED was effective in reducing or stopping the sub-clinical seizures, the systems of the present invention would be able to provide metrics for such a situation. With conventional subject diary “metrics”, the subject and physician would not be aware of such a reduction, and such an AED would be determined to be non-efficacious for the subject. However, because the present invention is able to provide metrics for the sub-clinical seizures, the efficacious medication could be continued.
At step 122, the epileptologist or neurologist may decide to change one or more parameters of the therapy. For example, they may change a dosage, frequency of dosage, form of the therapy or the like, and thereafter repeat the follow-up analysis for the therapy with the changed parameter. After the “second” follow up measurement is complete, the second follow up data may be obtained and thereafter compared to the “first” follow up measurements and/or the baseline measurements.
Of course, the therapy is not limited to AED therapy. Therapies that can be assessed by the present invention can include cooling therapy, electrical stimulation (such as vagus nerve stimulation, deep brain stimulation, cortical stimulation), or the like. The present invention may be used to screen the subject's for determining appropriate therapy for their condition and/or to determine the appropriate parameters for the selected therapy.
In addition to evaluating an efficacy of a therapy for an individual subject, the metrics that are provided by the present invention also enable an intelligent titration of a subject's medications. As shown in
At step 140, the subject's EEG is monitored and processed to obtain a second subject data measurement for the subject (e.g., follow-up data measurement). If the neurologist or epileptologist is satisfied with the results, the titration may end. But in many embodiments, the titration process will require more than one modification of parameters of the therapy. In such embodiments, the second therapy is stopped (step 142), and a therapy with Nth parameters (e.g., third, fourth, fifth . . . ) is commenced (step 144). Monitoring and processing of the subject's EEG signals are repeated (step 146), and the process is repeated a desired number of times (as illustrated by arrow 147). Once the desired numbers of modifications to the therapy have been made, the various subject data measurements may be analyzed and compared to each other to determine the most desirous parameters for the therapy (step 148).
With the instrumentation provided by the present invention, the process of selecting appropriate AEDs and the dosages of such AEDs could occur much faster and with much greater insight than ever before. Further, the chance of a subject remaining on an incremental AED that was providing little incremental benefit would be minimized. Once a subject was under control, the subject could cease the use of the system, but the implantable assembly could remain. In the future, the subject might be asked to use the system again should their condition change.
In addition to or as an alternative to the above data collection uses, the systems 10 of the present invention may be used to analyze EEG data substantially in real-time and provide an output to the subject and/or provide a therapy to the subject based on the analysis of the EEG data. In preferred embodiments, the systems of the present invention may be used as seizure advisory systems that measure the subject's susceptibility to a seizure and/or to detect the onset of the seizure prior to the clinical manifestation of the seizure and provide an appropriate warning to the subject.
The platform of system 10 used for data collection (described above) and the system used for determining the subject's susceptibility for having a seizure will generally have the same general components, so that the same system may be used for both data collection and advising of susceptibility to seizure. However, when the system is used for data collection during a training period, the algorithms that determine the subject's susceptibility of having a seizure may be disabled or not yet programmed in the system so as to not be accessible to the subject. If and when seizure advising is desired, such algorithms may be enabled and/or added into the system.
For example, EEG data may be collected as noted above. The collected EEG data may be analyzed off-line (e.g., in a separate computer, such as workstation 22) and, if desired, algorithms may be customized or otherwise tuned to the subject specific EEG data. Thereafter, the parameters of the disabled algorithm(s) may be modified or the entire tuned algorithm may be uploaded to a memory of system 10 and the aspects of the system relevant to seizure advising may be enabled. Finally, the seizure advising functionality in the system 10 may be enabled and used by the subject in real-time on a substantially continuous basis.
The input data 162 is typically EEG, but may comprise representations of physiological signals obtained from monitoring a subject and may comprise any one or combination of the aforementioned physiological signals from the subject. The input data may be in the form of analog signal data or digital signal data that has been converted by way of an analog to digital converter (not shown). The signals may also be amplified, preprocessed, and/or conditioned to filter out spurious signals or noise. For purposes of simplicity the input data of all of the preceding forms is referred to herein as input data 162. In one preferred embodiment, the input data comprises between about 1 channel and about 64 channels of EEG from the subject.
The input data 162 from the selected physiological signals is supplied to the one or more feature extractors 164a, 164b, 165. Feature extractor 164a, 164b, 165 may be, for example, a set of computer executable instructions stored on a computer readable medium, or a corresponding instantiated object or process that executes on a computing device. Certain feature extractors may also be implemented as programmable logic or as circuitry. In general, feature extractors 164a, 164b, 165 can process data 162 and identify some characteristic of interest in the data 162. Such a characteristic of the data is referred to herein as an extracted feature.
Each feature extractor 164a, 164b, 165 may be univariate (operating on a single input data channel), bivariate (operating on two data channels), or multivariate (operating on multiple data channels). Some examples of potentially useful characteristics to extract from signals for use in determining the subject's propensity for a neurological event, include but are not limited to, bandwidth limited power (alpha band [8-13 Hz], beta band [13-18 Hz], delta band [0.1-4 Hz], theta band [4-8 Hz], low beta band [12-15 Hz], mid-beta band [15-18 Hz], high beta band [18-30 Hz], gamma band [30-48 Hz], high frequency power [>48 Hz], bands with octave or half-octave spacings, wavelets, etc.), second, third and fourth (and higher) statistical moments of the EEG amplitudes or other features, spectral edge frequency, decorrelation time, Hjorth mobility (HM), Hjorth complexity (HC), the largest Lyapunov exponent L(max), effective correlation dimension, local flow, entropy, loss of recurrence LR as a measure of non-stationarity, mean phase coherence, conditional probability, brain dynamics (synchronization or desynchronization of neural activity, STLmax, T-index, angular frequency, and entropy), line length calculations, first, second and higher derivatives of amplitude or other features, integrals, and mathematical linear and non-linear operations including but not limited to addition, subtraction, division, multiplication and logarithmic operations. Of course, for other neurological conditions, additional or alternative characteristic extractors may be used with the systems described herein.
The extracted characteristics can be supplied to the one or more classifiers 166, 167. Like the feature extractors 164a, 164b, 165, each classifier 166, 167 may be, for example, a set of computer executable instructions stored on a computer readable medium or a corresponding instantiated object or process that executes on a computing device. Certain classifiers may also be implemented as programmable logic or as circuitry.
The classifiers 166, 167 analyze one or more of the extracted characteristics, and either alone or in combination with each other (and possibly other subject dependent parameters), provide a result 168 that may characterize, for example, a subject's condition. The output from the classifiers may then be used to determine the subject's susceptibility for having a seizure, which can determine the output communication that is provided to the subject regarding their condition. As described above, the classifiers 166, 167 are trained by exposing them to training measurement vectors, typically using supervised methods for known classes, e.g. ictal, and unsupervised methods as described above for classes that can't be identified a priori, e.g. contra-ictal. Some examples of classifiers include k-nearest neighbor (“KNN”), linear or non-linear regression, Bayesian, mixture models based on Gaussians or other basis functions, neural networks, and support vector machines (“SVM”). Each classifier 166, 167 may provide a variety of output results, such as a logical result or a weighted result. The classifiers 166, 167 may be customized for the individual subject and may be adapted to use only a subset of the characteristics that are most useful for the specific subject. Additionally, over time, the classifiers 166, 167 may be further adapted to the subject, based, for example, in part on the result of previous analyses and may reselect extracted characteristics that are used for the specific subject.
For the embodiment of
Depending on the specific feature extractors and classifiers used, the computational demands of the analysis provided by feature extractors 164a, 164b, 165 and classification provided by classifiers 166, 167 can be extensive. In the case of ambulatory systems supplied by portable power sources, such as batteries, supplying the power required to meet the computational demands can severely limit power source life. In preferred embodiments, both the seizure advisory algorithm are embodied in the external assembly 20. Processing the EEG data with the algorithms in the external assembly 20 provides a number of advantages over having the algorithms in the implanted assembly. First, keeping the processing in the external assembly 20 will reduce the overall power consumption in the implanted assembly 14 and will prolong the battery life of the implanted assembly 14. Second, charging of battery or replacing the battery of the external assembly 20 is much easier to accomplish. The battery of the external assembly may be charged by placing the external assembly 20 in a recharging cradle (e.g., inductive recharging) or simply by attaching the external assembly to an AC power source. Third, customizing, tuning and/or upgrading the algorithms will be easier to achieve in the external assembly 20. Such changes may be carried out by simply connecting the external assembly to the physician's computer workstation 20 and downloading the changes. Alternatively, upgrading may be performed automatically over a wireless connection with the communication sub-assembly 64.
While it is preferred to have the observer algorithms 160 in the external assembly 20, in alternate embodiments of the present invention, the observer algorithms 160 may be wholly embodied in the implanted assembly 14 or a portion of one or more of the observer algorithms 160 may be embodied in the implanted assembly 14 and another portion of the one or more algorithms may be embodied in the external assembly 20. In such embodiments, the processing sub-assembly 44 (or equivalent component) of the implanted assembly 14 may execute the analysis software, such as a seizure advisory algorithm(s) or portions of such algorithms. For example, in some configurations, one or more cores of the processing sub-assembly 44 may run one or more feature extractors that extract features from the EEG signal that are indicative of the subject's susceptibility to a seizure, while the classifier could run on a separate core of the processing sub-assembly 44. Once the feature(s) are extracted, the extracted feature(s) may be sent to the communication sub-assembly 46 for the wireless transmission to the external assembly 20 and/or store the extracted feature(s) in memory sub-system 52 of the implanted assembly 14. Because the transmission of the extracted features is likely to include less data than the EEG signal itself, such a configuration will likely reduce the bandwidth requirements for the wireless communication link 18 between the implantable assembly 14 and the external assembly 20.
In other embodiments, the seizure advisory algorithms may be wholly embodied within the implanted assembly 14 and the data transmission to the external assembly 29 may include the data output from the classifier, a warning signal, recommendation, or the like. A detailed discussion of various embodiments of the internal/external placement of such algorithms are described in commonly owned U.S. patent application Ser. No. 11/322,150, filed Dec. 28, 2005 to Bland et al., and U.S. Provisional Patent Application No. 60/805,710, filed Jun. 23, 2006, the complete disclosures of which are incorporated herein by reference.
At step 202, the system is used to collect EEG data for a desired time period, as described in detail above. Generally, the desired time period will be a specified time period such as at least one week, between one week and two weeks, between two weeks and one month, between one month and two months, or two months or more. But the desired time period may simply be a minimum time period that provides a desired number of seizure events. At step 204, the collected EEG data may be periodically downloaded to the physician's computer workstation or the entire EEG data may be brought into the physician's office in a single visit.
At step 206, the physician may analyze the EEG data using the computer workstation that is running EEG analysis software, the EEG data may be transferred to a remote analyzing facility that comprises a multiplicity of computing nodes where the EEG data may be analyzed on an expedited basis, or it may even be possible to analyze the EEG analysis software in the external assembly 20. Analysis of the EEG data may be performed in a piecewise fashion after the shorter epochs of EEG data is uploaded to the database, or the analysis of the EEG data may be started after the EEG data for the entire desired time period has been collected. Typically, “analysis of the EEG data” will include identifying and annotating at least some of spike bursts, the earliest electrographic change (EEC), unequivocal electrical onset (UEO), unequivocal clinical onset (UCO), electrographic end of seizure (EES). Identification of such events may be performed automatically with a seizure detection algorithm, manually by board certified epileptologists, or a combination thereof. After the EEG data is annotated, the seizure advisory algorithm(s) may be trained on the annotated EEG data in order to tune the parameters of the algorithm(s) to the subject specific EEG data.
Once the algorithm(s) are tuned to meet minimum performance criteria, at step 208 the tuned algorithm(s) or the parameter changes to the base algorithm may be uploaded to the external assembly 20. At step 210, the tuned algorithm and the other user interface aspects of the present invention may be activated, and the observer algorithm may be used by the subject to monitor the subject's susceptibility to a seizure and/or detect seizures.
When the seizure advisory system 10 determines that the subject is at an increased susceptibility to a seizure (or otherwise detects a seizure), the external assembly may be configured to generate a seizure warning to the subject, as described above. For example, the external assembly may activate a red or yellow LED light, generate a visual warning on the LCD, provide an audio warning, deliver a tactile warning, or any combination thereof. If desired, the warning may be “graded” so as to indicate the confidence level of the seizure advisory, indicate the estimated time horizon until the seizure, or the like. “Grading” of the warning may be through generation of different lights, audio, or tactile warning or a different pattern of lights, audio or tactile warnings.
Additionally or alternatively, the external assembly may include an instruction to the subject regarding an appropriate therapy for preventing or reducing the susceptibility for the seizure. The instruction may instruct the subject to take a dosage of their prescribed AED, perform biofeedback to prevent/abort the seizure, manually activate an electrical stimulator (e.g., use a wand to activate an implanted VNS device) or merely to instruct the subject to make themselves safe. A more complete description of various instructions that may be output to the subject are described in commonly owned, copending U.S. patent application Ser. Nos. 11/321,897 and 11/321,898, both of which are incorporated by reference herein.
The outputs provided to the subject via the external assembly may be a standardized warning or instruction, or it may be programmed by the physician to be customized specifically to the subject and their condition. For example, different subjects will be taking different AEDs, different dosages of the AEDs, and some may be implanted with manually actuatable stimulators (e.g., NeuroPace RNS, Cyberonics VNS, etc.), and the physician will likely be desirous to customize the therapy to the subject. Thus, the physician will be able to program the warning and/or instruction to correspond to the level of susceptibility, estimated time horizon to seizure, or the like.
The systems 10 of the present invention may also be adapted to provide closed-loop therapy to the subject.
While not shown in
Such therapies may be used in addition to the vagus nerve stimulation or as an alternative to such therapy. If desired, the type of therapy delivered to the subject may be modified based on the subject's susceptibility. For example, if the elevated susceptibility estimates a long time horizon until seizure and/or a lower confidence level, a more benign type of therapy (e.g., electrical stimulation) may be employed. But if the elevated susceptibility estimates a shorter time horizon until seizure and/or has a higher confidence level, a different type of therapy (e.g., pharmacotherapy) may be employed.
Some embodiments of the monitoring system may include an integral subject diary functionality. The subject diary may be a module in the external assembly and inputs by the subject may be used to provide secondary inputs to provide background information for the sampled EEG signals. For example, if a seizure is recorded, the seizure diary may provide insight regarding a trigger to the seizure, or the like. The diary may automatically record the time and date of the entry by the subject. Entries by the subject may be a voice recording, or through activation of user inputs on the external assembly. The diary may be used to indicate the occurrence of an aura, occurrence of a seizure, the consumption of a meal, missed meal, delayed meal, activities being performed, consumption of alcohol, the subject's sleep state (drowsy, going to sleep, waking up, etc.), mental state (e.g., depressed, excited, stressed), intake of their AEDs, medication changes, missed dosage of medication, menstrual cycle, illness, or the like. Thereafter, the subject inputs recorded in the diary may also be used by the physician in assessing the subject's epilepsy state and/or determine the efficacy of the current treatment. Furthermore, the physician may be able to compare the number of seizures logged by the subject to the number of seizures detected by the seizure detection algorithm.
While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. For example, the present invention also encompasses other more invasive embodiments which may be used to monitor the subject's neurological system.
It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.
1. A system for monitoring neurological signals in a patient, the system comprising:
- an implantable sensor adapted to collect neurological signals;
- an implantable assembly configured to sample the neurological signals collected by the sensor, wherein said implantable assembly comprises: a physical memory configured to store or buffer said neurological signals; a first telemetry circuit; a processing subassembly configured to process neurological signals prior to transmission to a rechargeable communication device external to the patient's body; and
- the rechargeable communication device external to the patient's body comprising: a display configured to indicate a neurological state of the patient; a second telemetry circuit; a processor configured to control outputs on the display and manage the second telemetry circuit, at least one of the outputs providing an indication of a change in a brain state of the patient; and
- wherein said rechargeable communication device is configured to wirelessly communicate with the implantable assembly and to transmit a communication error alert to a caregiver advisory device in an event of a communication error between the implantable assembly and the rechargeable communication device, wherein the caregiver advisory device allows at least one of monitoring the patient or facilitating treatment.
2. The system of claim 1, further comprising the caregiver advisory device, wherein the rechargeable communication device is configured to wirelessly communicate with the caregiver advisory device.
3. The system of claim 2, wherein the caregiver advisory device is configured to indicate if there is a communication error between the rechargeable communication device and the implantable assembly.
4. The system of claim 2, wherein the caregiver advisory device is configured to provide a visible alert if there is a communication error between the rechargeable communication device and the implantable assembly.
5. The system of claim 2, wherein the caregiver advisory device is configured to provide an audible alert if there is a communication error between the rechargeable communication device and the implantable assembly.
6. The system of claim 1, wherein the communication error comprises a failure of the rechargeable communication device to receive an expected data signal from the implantable assembly.
7. The system of claim 1, wherein the communication error comprises a failure of the rechargeable communication device to receive a data signal from the implantable assembly for a predetermined amount of time.
8. The system of claim 1, wherein the communication error comprises a failure of the rechargeable communication device to receive a data signal from the implantable assembly at an expected time or within an expected period of time.
9. The system of claim 1, wherein the communication error comprises a missing packet of data in a numbered sequence of packets transmitted from the implantable assembly to the rechargeable communication device.
10. The system of claim 1, wherein the rechargeable communication device is configured to automatically deactivate the communication error alert after a predetermined period of time.
11. The system of claim 1, wherein the implantable sensor is adapted to collect neurological signals from inside the patient's skull.
12. The system of claim 1, wherein the implantable sensor is adapted to collect neurological signals from a location between the patient's skull and at least a layer of the patient's scalp.
13. The system of claim 1, wherein the rechargeable communication device is configured to receive the neurological signals from the implantable assembly.
14. The system of claim 1, wherein the rechargeable communication device is configured to communicate with the caregiver advisory device via a wireless cellular network.
15. The system of claim 1, wherein the rechargeable communication device is configured to communicate with the caregiver advisory device via a phone call.
16. The system of claim 1, wherein the implantable assembly is configured to transmit the neurological signals to the rechargeable communication device substantially immediately after sampling.
17. A method of monitoring neurological signals in a patient, comprising:
- collecting neurological signals using a sensor implanted in the patient;
- sampling the neurological signals using an implantable assembly;
- storing or buffering said neurological signals in memory of the implanted assembly;
- processing said neurological signals prior to transmission to an external rechargeable communication device using a processor subassembly;
- displaying a neurological state of the patient using the rechargeable communication device;
- controlling outputs on the rechargeable communication device, at least one of the outputs providing an indication of a change in a brain state of the patient and managing a telemetry circuit using a processor on said rechargeable communication device;
- indicating a change in brain state of the patient based on the neurological signals via an output on said rechargeable communication device;
- wirelessly transmitting a communication error alert from the rechargeable communication device external to the patient's body to a caregiver advisory device in the event of a communication error between the implantable assembly and the rechargeable communication device wherein the caregiver advisory device allows for at least one of monitoring the patient or facilitating treatment.
18. The method of claim 17, further comprising wirelessly communicating from the rechargeable communication device to the caregiver advisory device.
19. The method of claim 18, further comprising generating an indication from the caregiver advisory device if there is a communication error between the rechargeable communication device and the implantable assembly.
20. The method of claim 18, further comprising providing a visible alert from the caregiver advisory device if there is a communication error between the rechargeable communication device and the implantable assembly.
21. The method of claim 18, further comprising providing a visible alert from the caregiver advisory device if there is a communication error between the rechargeable communication device and the implantable assembly.
22. The method of claim 17, wherein said communication error comprises a failure of the rechargeable communication device to receive an expected data signal from the implantable assembly.
23. The method of claim 17, wherein said communication error comprises a failure of the rechargeable communication device to receive a data signal from the implantable assembly for a predetermined amount of time.
24. The method of claim 17, wherein said communication error comprises a failure of the rechargeable communication device to receive a data signal from the implantable assembly at an expected time or within an expected period of time.
25. The method of claim 17, wherein said communication error comprises a missing packet of data in a numbered sequence of packets transmitted from the implantable assembly to the rechargeable communication device.
26. The method of claim 17, further comprising automatically deactivating the communication error alert after a predetermined period of time.
27. The method of claim 17, wherein said collecting neurological signals comprises collecting neurological signals from inside the patient's skull.
28. The method of claim 17, wherein said collecting neurological signals comprises collecting neurological signals from a location between the patient's skull and at least a layer of the patient's scalp.
29. The method of claim 17, further comprising receiving at the rechargeable communication device the neurological signals from the implantable assembly.
30. The method of claim 17, wherein said transmitting the communication error alert comprises transmitting the communication error alert from the rechargeable communication device to the caregiver advisory device via a wireless cellular network.
31. The method of claim 17, wherein said transmitting the communication error alert comprises transmitting the communication error alert from the rechargeable communication device to the caregiver advisory device via a phone call.
32. The method of claim 17, further comprising transmitting the neurological signals from the implantable assembly to the rechargeable communication device substantially immediately after sampling.
|3863625||February 1975||Viglione et al.|
|3882850||May 1975||Bailin et al.|
|3967616||July 6, 1976||Ross|
|3993046||November 23, 1976||Fernandez|
|4201224||May 6, 1980||John|
|4214591||July 29, 1980||Sato et al.|
|4279258||July 21, 1981||John|
|4305402||December 15, 1981||Katims|
|4334545||June 15, 1982||Shiga|
|4407299||October 4, 1983||Culver|
|4408616||October 11, 1983||Duffy et al.|
|4421122||December 20, 1983||Duffy|
|4471786||September 18, 1984||Inagaki|
|4494950||January 22, 1985||Fischell|
|4505275||March 19, 1985||Chen|
|4545388||October 8, 1985||John|
|4556061||December 3, 1985||Barreras et al.|
|4566464||January 28, 1986||Piccone et al.|
|4573481||March 4, 1986||Bullara|
|4579125||April 1, 1986||Strobl et al.|
|4590946||May 27, 1986||Loeb|
|4612934||September 23, 1986||Borkan|
|4679144||July 7, 1987||Cox et al.|
|4686999||August 18, 1987||Snyder et al.|
|4702254||October 27, 1987||Zabara|
|4735208||April 5, 1988||Wyler et al.|
|4768176||August 30, 1988||Kehr et al.|
|4768177||August 30, 1988||Kehr et al.|
|4785827||November 22, 1988||Fischer|
|4793353||December 27, 1988||Borkam|
|4817628||April 4, 1989||Zealear|
|4838272||June 13, 1989||Lieber|
|4844075||July 4, 1989||Liss et al.|
|4852573||August 1, 1989||Kennedy|
|4867164||September 19, 1989||Zabara|
|4873981||October 17, 1989||Abrams et al.|
|4878498||November 7, 1989||Abrams et al.|
|4903702||February 27, 1990||Putz|
|4920979||May 1, 1990||Bullara|
|4926865||May 22, 1990||Oman|
|4934372||June 19, 1990||Corenman et al.|
|4955380||September 11, 1990||Edell|
|4978680||December 18, 1990||Sofia|
|4979511||December 25, 1990||Terry|
|4991582||February 12, 1991||Byers et al.|
|5010891||April 30, 1991||Chamoun|
|5016635||May 21, 1991||Graupe|
|5025807||June 25, 1991||Zabara|
|5031618||July 16, 1991||Mullett|
|5070873||December 10, 1991||Graupe et al.|
|5082861||January 21, 1992||Sofia|
|5097835||March 24, 1992||Putz|
|RE34015||August 4, 1992||Duffy|
|5154172||October 13, 1992||Terry|
|5167229||December 1, 1992||Peckham et al.|
|5179950||January 19, 1993||Stanislaw|
|5181520||January 26, 1993||Wertheim et al.|
|5186170||February 16, 1993||Varichio|
|5188104||February 23, 1993||Wernicke|
|5190029||March 2, 1993||Byron et al.|
|5193539||March 16, 1993||Schulman et al.|
|5193540||March 16, 1993||Schulman et al.|
|5205285||April 27, 1993||Baker, Jr.|
|5215086||June 1, 1993||Terry, Jr. et al.|
|5215088||June 1, 1993||Normann|
|5215089||June 1, 1993||Baker, Jr.|
|5222494||June 29, 1993||Baker, Jr.|
|5222503||June 29, 1993||Ives|
|5231988||August 3, 1993||Wernicke et al.|
|5235980||August 17, 1993||Varichio et al.|
|5237991||August 24, 1993||Baker, Jr.|
|5251634||October 12, 1993||Weinberg|
|5263480||November 23, 1993||Wernicke et al.|
|5265619||November 30, 1993||Comby et al.|
|5269302||December 14, 1993||Swartz et al.|
|5269303||December 14, 1993||Wernicke et al.|
|5269315||December 14, 1993||Leuchter et al.|
|5292772||March 8, 1994||Sofia|
|5293879||March 15, 1994||Vonk|
|5299118||March 29, 1994||Martens et al.|
|5299569||April 5, 1994||Wernicke et al.|
|5300094||April 5, 1994||Kallok et al.|
|5304206||April 19, 1994||Baker, Jr. et al.|
|5311876||May 17, 1994||Olsen et al.|
|5312439||May 17, 1994||Loeb|
|5314458||May 24, 1994||Najafi et al.|
|5324316||June 28, 1994||Schulman et al.|
|5330515||July 19, 1994||Rutecki et al.|
|5335657||August 9, 1994||Terry et al.|
|5342408||August 30, 1994||deCorlolis et al.|
|5342409||August 30, 1994||Mullett|
|5343064||August 30, 1994||Spangler et al.|
|5349962||September 27, 1994||Lockard et al.|
|5351394||October 4, 1994||Weinberg|
|5361760||November 8, 1994||Normann|
|5365939||November 22, 1994||Ochs|
|5376359||December 27, 1994||Johnson|
|5392788||February 28, 1995||Hudspeth|
|5405365||April 11, 1995||Hoegnelid et al.|
|5405367||April 11, 1995||Schulman et al.|
|5411540||May 2, 1995||Edell et al.|
|5458117||October 17, 1995||Chamoun|
|5474547||December 12, 1995||Aebischer et al.|
|5476494||December 19, 1995||Edell et al.|
|5486999||January 23, 1996||Mebane|
|5513649||May 7, 1996||Gevins|
|5517115||May 14, 1996||Prammer|
|5531778||July 2, 1996||Maschino et al.|
|5540730||July 30, 1996||Terry|
|5540734||July 30, 1996||Zabara|
|5549656||August 27, 1996||Reiss|
|5555191||September 10, 1996||Hripcsak|
|5571148||November 5, 1996||Loeb et al.|
|5571150||November 5, 1996||Wernicke|
|5575813||November 19, 1996||Edell et al.|
|5578036||November 26, 1996||Stone et al.|
|5611350||March 18, 1997||John|
|5626145||May 6, 1997||Clapp et al.|
|5626627||May 6, 1997||Krystal et al.|
|5638826||June 17, 1997||Wolpaw|
|5649068||July 15, 1997||Boser et al.|
|5672154||September 30, 1997||Sillen et al.|
|5683422||November 4, 1997||Rise|
|5683432||November 4, 1997||Goedeke et al.|
|5690681||November 25, 1997||Geddes et al.|
|5690691||November 25, 1997||Chen et al.|
|5697369||December 16, 1997||Long|
|5700282||December 23, 1997||Zabara|
|5704352||January 6, 1998||Tremblay et al.|
|5707400||January 13, 1998||Terry et al.|
|5711316||January 27, 1998||Elsberry et al.|
|5713923||February 3, 1998||Ward et al.|
|5715821||February 10, 1998||Faupel|
|5716377||February 10, 1998||Rise et al.|
|5720294||February 24, 1998||Skinner|
|5735814||April 7, 1998||Elsberry et al.|
|5743860||April 28, 1998||Hively et al.|
|5752979||May 19, 1998||Benabid|
|5769778||June 23, 1998||Abrams et al.|
|5776434||July 7, 1998||Purewal et al.|
|5782798||July 21, 1998||Rise|
|5782874||July 21, 1998||Loos|
|5782891||July 21, 1998||Hassler et al.|
|5792186||August 11, 1998||Rise|
|5800474||September 1, 1998||Bernabid et al.|
|5813993||September 29, 1998||Kaplan|
|5814014||September 29, 1998||Elsberry et al.|
|5815413||September 29, 1998||Hively et al.|
|5816247||October 6, 1998||Maynard|
|5824021||October 20, 1998||Rise|
|5832932||November 10, 1998||Elsberry et al.|
|5833709||November 10, 1998||Rise et al.|
|5857978||January 12, 1999||Hively et al.|
|5862803||January 26, 1999||Besson et al.|
|5876424||March 2, 1999||O'Phelan et al.|
|5899922||May 4, 1999||Loos|
|5913881||June 22, 1999||Benz et al.|
|5916239||June 29, 1999||Geddes et al.|
|5917429||June 29, 1999||Otis, Jr. et al.|
|5928272||July 27, 1999||Adkins|
|5931791||August 3, 1999||Saltzstein et al.|
|5938689||August 17, 1999||Fischell et al.|
|5941906||August 24, 1999||Barreras et al.|
|5950632||September 14, 1999||Reber et al.|
|5957861||September 28, 1999||Combs et al.|
|5971594||October 26, 1999||Sahai et al.|
|5975085||November 2, 1999||Rise|
|5978702||November 2, 1999||Ward et al.|
|5978710||November 2, 1999||Prutchi et al.|
|5995868||November 30, 1999||Dorfmeister et al.|
|6006124||December 21, 1999||Fischell et al.|
|6016449||January 18, 2000||Fischell et al.|
|6018682||January 25, 2000||Rise|
|6042548||March 28, 2000||Giuffre|
|6042579||March 28, 2000||Elsberry et al.|
|6051017||April 18, 2000||Loeb et al.|
|6052619||April 18, 2000||John|
|6061593||May 9, 2000||Fischell et al.|
|6066163||May 23, 2000||John|
|6081744||June 27, 2000||Loos|
|6094598||July 25, 2000||Elsberry et al.|
|6108571||August 22, 2000||Minoz et al.|
|6109269||August 29, 2000||Rise et al.|
|6117066||September 12, 2000||Abrams et al.|
|6128537||October 3, 2000||Rise et al.|
|6128538||October 3, 2000||Fischell et al.|
|6134474||October 17, 2000||Fischell et al.|
|6161045||December 12, 2000||Fischell et al.|
|6167304||December 26, 2000||Loos|
|6171239||January 9, 2001||Humphrey|
|6176242||January 23, 2001||Rise|
|6205359||March 20, 2001||Boveja|
|6208893||March 27, 2001||Hofmann|
|6221011||April 24, 2001||Bardy|
|6227203||May 8, 2001||Rise et al.|
|6230049||May 8, 2001||Fischell et al.|
|6248126||June 19, 2001||Lesser et al.|
|6249703||June 19, 2001||Stanton|
|6263237||July 17, 2001||Rise|
|6280198||August 28, 2001||Calhoun et al.|
|6304775||October 16, 2001||Iasemidis et al.|
|6309406||October 30, 2001||Jones et al.|
|6328699||December 11, 2001||Eigler|
|6337997||January 8, 2002||Rise|
|6339725||January 15, 2002||Naritoku|
|6341236||January 22, 2002||Osorio et al.|
|6343226||January 29, 2002||Sunde et al.|
|6353754||March 5, 2002||Fischell et al.|
|6354299||March 12, 2002||Fischell et al.|
|6356784||March 12, 2002||Lozano et al.|
|6356788||March 12, 2002||Boveja|
|6358203||March 19, 2002||Bardy|
|6358281||March 19, 2002||Berrang et al.|
|6360122||March 19, 2002||Fischell|
|6366813||April 2, 2002||DiLorenzo|
|6366814||April 2, 2002||Boveja|
|6374140||April 16, 2002||Rise|
|6386882||May 14, 2002||Linberg|
|6402678||June 11, 2002||Fischell et al.|
|6411854||June 25, 2002||Tziviskos et al.|
|6427086||July 30, 2002||Fischell et al.|
|6434419||August 13, 2002||Gevins et al.|
|6442421||August 27, 2002||Quyen et al.|
|6443890||September 3, 2002||Schulze|
|6443891||September 3, 2002||Grevious|
|6453198||September 17, 2002||Torgerson|
|6463328||October 8, 2002||John|
|6466822||October 15, 2002||Pless|
|6471645||October 29, 2002||Warkentin et al.|
|6473639||October 29, 2002||Fischell et al.|
|6473644||October 29, 2002||Terry et al.|
|6480743||November 12, 2002||Kirkpatrick|
|6484132||November 19, 2002||Hively et al.|
|6488617||December 3, 2002||Katz|
|6496724||December 17, 2002||Levendowski et al.|
|6505077||January 7, 2003||Kast et al.|
|6510340||January 21, 2003||Jordan|
|6511424||January 28, 2003||Moore-Ede|
|6529774||March 4, 2003||Greene|
|6534693||March 18, 2003||Fischell et al.|
|6547746||April 15, 2003||Marino|
|6549804||April 15, 2003||Osorio et al.|
|6553262||April 22, 2003||Lang et al.|
|6560486||May 6, 2003||Osorio et al.|
|6571123||May 27, 2003||Ives et al.|
|6571125||May 27, 2003||Thompson|
|6572528||June 3, 2003||Rohan et al.|
|6587719||July 1, 2003||Barrett et al.|
|6591132||July 8, 2003||Gotman et al.|
|6591137||July 8, 2003||Fischell et al.|
|6591138||July 8, 2003||Fischell et al.|
|6594524||July 15, 2003||Esteller et al.|
|6597954||July 22, 2003||Pless et al.|
|6600956||July 29, 2003||Maschino|
|6609025||August 19, 2003||Barrett et al.|
|6618623||September 9, 2003||Pless et al.|
|6620415||September 16, 2003||Donovan|
|6622036||September 16, 2003||Suffin|
|6622038||September 16, 2003||Barrett et al.|
|6622041||September 16, 2003||Terry et al.|
|6622047||September 16, 2003||Barrett et al.|
|6658287||December 2, 2003||Litt et al.|
|6665562||December 16, 2003||Gluckman et al.|
|6668191||December 23, 2003||Boveja|
|6671555||December 30, 2003||Gielen|
|6678548||January 13, 2004||Echauz et al.|
|6684105||January 27, 2004||Cohen et al.|
|6687538||February 3, 2004||Hrdlicka et al.|
|6735467||May 11, 2004||Wilson|
|6760626||July 6, 2004||Boveja|
|6768969||July 27, 2004||Nikitin et al.|
|6778854||August 17, 2004||Puskas|
|6782292||August 24, 2004||Whitehurst|
|6819956||November 16, 2004||DiLorenzo|
|6879859||April 12, 2005||Boveja|
|6893395||May 17, 2005||Kraus et al.|
|6901294||May 31, 2005||Whitehurst et al.|
|6901296||May 31, 2005||Whitehurst et al.|
|6912419||June 28, 2005||Hill|
|6921538||July 26, 2005||Donovan|
|6921541||July 26, 2005||Chasin et al.|
|6923784||August 2, 2005||Stein|
|6931274||August 16, 2005||Williams|
|6934580||August 23, 2005||Osorio|
|6937891||August 30, 2005||Leinders et al.|
|6944501||September 13, 2005||Pless|
|6950706||September 27, 2005||Rodriguez|
|6973342||December 6, 2005||Swanson|
|6990372||January 24, 2006||Perron et al.|
|7010351||March 7, 2006||Firlik et al.|
|7089059||August 8, 2006||Pless|
|7117108||October 3, 2006||Rapp et al.|
|7174212||February 6, 2007||Klehn et al.|
|7177701||February 13, 2007||Pianca|
|7209787||April 24, 2007||DiLorenzo|
|7212851||May 1, 2007||Donoghue et al.|
|7231254||June 12, 2007||DiLorenzo|
|7242984||July 10, 2007||DiLorenzo|
|7277758||October 2, 2007||DiLorenzo|
|7294105||November 13, 2007||Islam|
|7324851||January 29, 2008||DiLorenzo|
|7373198||May 13, 2008||Bibian et al.|
|7403820||July 22, 2008||DiLorenzo|
|7463917||December 9, 2008||Martinez|
|7483743||January 27, 2009||Mann et al.|
|7623928||November 24, 2009||DiLorenzo|
|7631015||December 8, 2009||Gupta et al.|
|7747325||June 29, 2010||Dilorenzo|
|7805196||September 28, 2010||Miesel et al.|
|7881798||February 1, 2011||Miesel et al.|
|8055348||November 8, 2011||Heruth et al.|
|20010051819||December 13, 2001||Fischell et al.|
|20010056290||December 27, 2001||Fischell et al.|
|20020002390||January 3, 2002||Fischell et al.|
|20020035338||March 21, 2002||Dear et al.|
|20020054694||May 9, 2002||Vachtsevanos et al.|
|20020072770||June 13, 2002||Pless|
|20020072776||June 13, 2002||Osorio et al.|
|20020072782||June 13, 2002||Osorio et al.|
|20020077670||June 20, 2002||Archer et al.|
|20020095099||July 18, 2002||Quyen et al.|
|20020099412||July 25, 2002||Fischell et al.|
|20020103512||August 1, 2002||Echauz et al.|
|20020109621||August 15, 2002||Khair et al.|
|20020111542||August 15, 2002||Warkentin et al.|
|20020116042||August 22, 2002||Boling|
|20020126036||September 12, 2002||Flaherty et al.|
|20020147388||October 10, 2002||Mass et al.|
|20020169485||November 14, 2002||Pless et al.|
|20030004428||January 2, 2003||Pless|
|20030009207||January 9, 2003||Paspa et al.|
|20030013981||January 16, 2003||Gevins et al.|
|20030018367||January 23, 2003||DiLorenzo|
|20030028072||February 6, 2003||Fischell et al.|
|20030050549||March 13, 2003||Sochor|
|20030050730||March 13, 2003||Greeven et al.|
|20030073917||April 17, 2003||Echauz et al.|
|20030074033||April 17, 2003||Pless et al.|
|20030083716||May 1, 2003||Nicolelis et al.|
|20030114886||June 19, 2003||Gluckman et al.|
|20030144709||July 31, 2003||Zabara et al.|
|20030144711||July 31, 2003||Pless et al.|
|20030144829||July 31, 2003||Geatz et al.|
|20030149457||August 7, 2003||Tcheng et al.|
|20030158587||August 21, 2003||Esteller et al.|
|20030167078||September 4, 2003||Weisner et al.|
|20030174554||September 18, 2003||Dunstone et al.|
|20030176806||September 18, 2003||Pineda et al.|
|20030187621||October 2, 2003||Nikitin et al.|
|20030195574||October 16, 2003||Osorio et al.|
|20030195588||October 16, 2003||Fischell et al.|
|20030195602||October 16, 2003||Boling|
|20040034368||February 19, 2004||Pless et al.|
|20040039427||February 26, 2004||Barrett et al.|
|20040039981||February 26, 2004||Riedl et al.|
|20040054297||March 18, 2004||Wingeier et al.|
|20040059761||March 25, 2004||Hively|
|20040068199||April 8, 2004||Echauz et al.|
|20040073273||April 15, 2004||Gluckman et al.|
|20040077995||April 22, 2004||Ferek-Petric|
|20040078160||April 22, 2004||Frei et al.|
|20040082984||April 29, 2004||Osorio et al.|
|20040087835||May 6, 2004||Hively|
|20040097802||May 20, 2004||Cohen|
|20040122281||June 24, 2004||Fischell et al.|
|20040122335||June 24, 2004||Sackellares et al.|
|20040122488||June 24, 2004||Mazar et al.|
|20040127810||July 1, 2004||Sackellares et al.|
|20040133119||July 8, 2004||Osorio et al.|
|20040133248||July 8, 2004||Frei et al.|
|20040133390||July 8, 2004||Osorio et al.|
|20040138516||July 15, 2004||Osorio et al.|
|20040138517||July 15, 2004||Osorio et al.|
|20040138536||July 15, 2004||Frei et al.|
|20040138578||July 15, 2004||Pineda et al.|
|20040138579||July 15, 2004||Deadwyler et al.|
|20040138580||July 15, 2004||Frei et al.|
|20040138581||July 15, 2004||Frei et al.|
|20040138647||July 15, 2004||Osorio et al.|
|20040138711||July 15, 2004||Osorio et al.|
|20040138721||July 15, 2004||Osorio et al.|
|20040147969||July 29, 2004||Mann et al.|
|20040152958||August 5, 2004||Frei et al.|
|20040153129||August 5, 2004||Pless et al.|
|20040158119||August 12, 2004||Osorio et al.|
|20040172089||September 2, 2004||Whitehurst et al.|
|20040176359||September 9, 2004||Wermeling|
|20040181263||September 16, 2004||Balzer et al.|
|20040199212||October 7, 2004||Fischell|
|20040210269||October 21, 2004||Shalev et al.|
|20040243146||December 2, 2004||Chesbrough et al.|
|20040267152||December 30, 2004||Pineda et al.|
|20050004621||January 6, 2005||Boveja et al.|
|20050010113||January 13, 2005||Hall et al.|
|20050010261||January 13, 2005||Luders et al.|
|20050015128||January 20, 2005||Rezai et al.|
|20050015129||January 20, 2005||Mische|
|20050021105||January 27, 2005||Firlik et al.|
|20050021108||January 27, 2005||Klosterman et al.|
|20050021313||January 27, 2005||Nikitin et al.|
|20050027328||February 3, 2005||Greenstein|
|20050033369||February 10, 2005||Badelt|
|20050043772||February 24, 2005||Stahmann et al.|
|20050043774||February 24, 2005||Devlin et al.|
|20050049649||March 3, 2005||Luders et al.|
|20050059867||March 17, 2005||Cheng|
|20050070970||March 31, 2005||Knudson et al.|
|20050075067||April 7, 2005||Lawson et al.|
|20050096710||May 5, 2005||Kieval|
|20050113885||May 26, 2005||Haubrich et al.|
|20050124863||June 9, 2005||Cook|
|20050131493||June 16, 2005||Boveja et al.|
|20050137640||June 23, 2005||Freeberg et al.|
|20050143786||June 30, 2005||Boveja|
|20050143787||June 30, 2005||Boveja et al.|
|20050149123||July 7, 2005||Lesser et al.|
|20050182308||August 18, 2005||Bardy|
|20050182464||August 18, 2005||Schulte et al.|
|20050187789||August 25, 2005||Hatlestad|
|20050197590||September 8, 2005||Osorio et al.|
|20050203366||September 15, 2005||Donoghue et al.|
|20050203584||September 15, 2005||Twetan et al.|
|20050209218||September 22, 2005||Meyerson et al.|
|20050222503||October 6, 2005||Dunlop et al.|
|20050222641||October 6, 2005||Pless|
|20050228249||October 13, 2005||Boling|
|20050228461||October 13, 2005||Osorio et al.|
|20050231374||October 20, 2005||Diem et al.|
|20050234355||October 20, 2005||Rowlandson|
|20050240245||October 27, 2005||Bange et al.|
|20050245970||November 3, 2005||Erickson et al.|
|20050245971||November 3, 2005||Brockway et al.|
|20050245984||November 3, 2005||Singhal et al.|
|20050266301||December 1, 2005||Smith et al.|
|20050277844||December 15, 2005||Strother|
|20060015034||January 19, 2006||Martinerie et al.|
|20060015153||January 19, 2006||Gliner et al.|
|20060094970||May 4, 2006||Drew|
|20060111644||May 25, 2006||Guttag et al.|
|20060122469||June 8, 2006||Martel|
|20060129056||June 15, 2006||Leuthardt et al.|
|20060136006||June 22, 2006||Giftakis et al.|
|20060142822||June 29, 2006||Tulgar|
|20060173259||August 3, 2006||Flaherty et al.|
|20060173510||August 3, 2006||Besio et al.|
|20060200038||September 7, 2006||Savit et al.|
|20060212092||September 21, 2006||Pless et al.|
|20060212093||September 21, 2006||Pless et al.|
|20060212096||September 21, 2006||Stevenson|
|20060217792||September 28, 2006||Hussein et al.|
|20060224191||October 5, 2006||Dilorenzo|
|20060253096||November 9, 2006||Blakley et al.|
|20060293578||December 28, 2006||Rennaker, II|
|20060293720||December 28, 2006||DiLorenzo et al.|
|20070027367||February 1, 2007||Oliver et al.|
|20070027514||February 1, 2007||Gerber|
|20070035910||February 15, 2007||Stevenson|
|20070043459||February 22, 2007||Abbott, III et al.|
|20070055320||March 8, 2007||Weinand|
|20070060973||March 15, 2007||Ludvig et al.|
|20070073355||March 29, 2007||DiLorenzo|
|20070073357||March 29, 2007||Rooney et al.|
|20070100398||May 3, 2007||Sloan|
|20070149952||June 28, 2007||Bland et al.|
|20070150024||June 28, 2007||Leyde et al.|
|20070150025||June 28, 2007||DiLorenzo et al.|
|20070161919||July 12, 2007||DiLorenzo|
|20070162086||July 12, 2007||DiLorenzo|
|20070167991||July 19, 2007||DiLorenzo|
|20070185890||August 9, 2007||VanEpps et al.|
|20070197878||August 23, 2007||Shklarski|
|20070213629||September 13, 2007||Greene|
|20070213785||September 13, 2007||Osorio et al.|
|20070217121||September 20, 2007||Fu et al.|
|20070238939||October 11, 2007||Giftakis et al.|
|20070244407||October 18, 2007||Osorio|
|20070250077||October 25, 2007||Skakoon et al.|
|20070250901||October 25, 2007||McIntire et al.|
|20070287931||December 13, 2007||DiLorenzo|
|20070293774||December 20, 2007||Acquista|
|20080021341||January 24, 2008||Harris et al.|
|20080027347||January 31, 2008||Harris et al.|
|20080027348||January 31, 2008||Harris et al.|
|20080027515||January 31, 2008||Harris et al.|
|20080033502||February 7, 2008||Harris et al.|
|20080082019||April 3, 2008||Ludving et al.|
|20080091090||April 17, 2008||Guillory et al.|
|20080103556||May 1, 2008||Li et al.|
|20080114417||May 15, 2008||Leyde|
|20080119900||May 22, 2008||DiLorenzo|
|20080161712||July 3, 2008||Leyde|
|20080161713||July 3, 2008||Leyde et al.|
|20080183057||July 31, 2008||Taube|
|20080183096||July 31, 2008||Snyder et al.|
|20080183097||July 31, 2008||Leyde et al.|
|20080208074||August 28, 2008||Snyder et al.|
|20080221876||September 11, 2008||Holdrich|
|20080234598||September 25, 2008||Snyder et al.|
|20080255582||October 16, 2008||Harris|
|20080273287||November 6, 2008||Iyer et al.|
|20080288023||November 20, 2008||John|
|20080319281||December 25, 2008||Aarts|
|20090018609||January 15, 2009||DiLorenzo|
|20090062682||March 5, 2009||Bland et al.|
|20090062696||March 5, 2009||Nathan et al.|
|20090171168||July 2, 2009||Leyde et al.|
|20090171420||July 2, 2009||Brown et al.|
|20090264952||October 22, 2009||Jassemidis et al.|
|20100023089||January 28, 2010||DiLorenzo|
|20100125219||May 20, 2010||Harris et al.|
|20100145176||June 10, 2010||Himes|
|20100168603||July 1, 2010||Himes et al.|
|20100168604||July 1, 2010||Echauz et al.|
|20100179627||July 15, 2010||Floyd et al.|
|20100217348||August 26, 2010||DiLorenzo|
|20100292602||November 18, 2010||Worrell et al.|
|20100302270||December 2, 2010||Echauz et al.|
|20110166430||July 7, 2011||Harris et al.|
|20110172554||July 14, 2011||Leyde et al.|
|20110201944||August 18, 2011||Higgins et al.|
|20110260855||October 27, 2011||John et al.|
|20110319785||December 29, 2011||Snyder et al.|
|WO 85/01213||March 1985||WO|
|WO 92/00119||January 1992||WO|
|WO 97/26823||July 1997||WO|
|WO 97/34522||September 1997||WO|
|WO 97/34524||September 1997||WO|
|WO 97/34525||September 1997||WO|
|WO 97/39797||October 1997||WO|
|WO 97/42990||November 1997||WO|
|WO 97/45160||December 1997||WO|
|WO 98/49935||November 1998||WO|
|WO 99/20342||April 1999||WO|
|WO 99/56821||November 1999||WO|
|WO 99/56822||November 1999||WO|
|WO 00/07494||February 2000||WO|
|WO 00/10455||March 2000||WO|
|WO 01/41867||June 2001||WO|
|WO 01/48676||July 2001||WO|
|WO 01/49364||July 2001||WO|
|WO 01/67288||September 2001||WO|
|WO 01/75660||October 2001||WO|
|WO 02/09610||February 2002||WO|
|WO 02/09811||February 2002||WO|
|WO 02/36003||May 2002||WO|
|WO 02/38031||May 2002||WO|
|WO 02/38217||May 2002||WO|
|WO 02/49500||June 2002||WO|
|WO 02/058536||August 2002||WO|
|WO 02/067122||August 2002||WO|
|WO 03/001996||January 2003||WO|
|WO 03/009207||January 2003||WO|
|WO 03/030734||April 2003||WO|
|WO 03/035165||May 2003||WO|
|WO 03/084605||October 2003||WO|
|WO 2004/008373||January 2004||WO|
|WO 2004/032720||April 2004||WO|
|WO 2004/034231||April 2004||WO|
|WO 2004/034879||April 2004||WO|
|WO 2004/034880||April 2004||WO|
|WO 2004/034881||April 2004||WO|
|WO 2004/034882||April 2004||WO|
|WO 2004/034883||April 2004||WO|
|WO 2004/034885||April 2004||WO|
|WO 2004/034982||April 2004||WO|
|WO 2004/034997||April 2004||WO|
|WO 2004/034998||April 2004||WO|
|WO 2004/035130||April 2004||WO|
|WO 2004/036370||April 2004||WO|
|WO 2004/036372||April 2004||WO|
|WO 2004/036376||April 2004||WO|
|WO 2004/036377||April 2004||WO|
|WO 2004/037342||May 2004||WO|
|WO 2004/043536||May 2004||WO|
|WO 2004/091718||October 2004||WO|
|WO 2005/007236||January 2005||WO|
|WO 2005/028026||March 2005||WO|
|WO 2005/028028||March 2005||WO|
|WO 2005/031630||April 2005||WO|
|WO 2005/051167||June 2005||WO|
|WO 2005/051306||June 2005||WO|
|WO 2005/117693||December 2005||WO|
|WO 2006/014971||February 2006||WO|
|WO 2006/014972||February 2006||WO|
|WO 2006/020794||February 2006||WO|
- Leyde et al.; U.S. Appl. No. 13/441,609 entitled “Multi-Channel Amplifier Techniques,” filed Apr. 6, 2012.
- Spector et al.; High and Low Perceived Self-Control of Epileptic Seizures; Epilepsia, vol. 42(4), Apr. 2001; pp. 556-564.
- Adjouadi, et al. A new mathematical approach based on orthogonal operators for the detection of interictal spikes in epileptogenic data. Biomed. Sci. Instrum. 2004; 40: 175-80.
- Adjouadi, et al. Detection of interictal spikes and artifactual data through orthogonal transformations. J. Clin. Neurophysiol. 2005; 22(1):53-64.
- Adjouadi, et al. Interictal spike detection using the Walsh transform. IEEE Trans. Biomed. Eng. 2004; 51(5): 868-72.
- Aksenova, et al. Nonparametric on-line detection of changes in signal spectral characteristics for early prediction of epilepsy seizure onset. J. Automation and Information Sciences. 2004; 36(8): 35-45.
- Aksenova, et al. On-line disharmony detection for early prediction of epilepsy seizure onset. 5th International Workshop Neural Coding 2003. Aulla (Italy) Sep. 20-25, 2003. (Abstract).
- Andrzejak, et al. Bivariate surrogate techniques: necessity, strengths, and caveats. Physical Review E. 2003; 68: 066202-1-066202-15.
- Andrzejak, et al. Testing the null hypothesis of the nonexistence of a preseizure state. Physical Review E. 2003; 67: 010901-1-010901-4.
- Aschenbrenner-Scheibe, et al. How well can epileptic seizures be predicted? An evaluation of a nonlinear method. Brain. 2003; 126: 2616-26.
- Bangham et al. Diffusion of univalent ions across the lamellae of swollen phospholipids. 1965. J Mol. Biol. 13: 238-252.
- Baruchi, et al. Functional holography of complex networks activity—From cultures to the human brain. Complexity. 2005; 10(3): 38 R 51.
- Baruchi, et al. Functional holography of recorded neuronal networks activity. Neuroinformatics. 2004; 2(3): 333-51.
- Ben-Hur, et al. Detecting stable clusters using principal component analysis. Methods Mol. Biol. 2003; 224: 159-82.
- Bergey, et al. Epileptic seizures are characterized by changing signal complexity. Clin. Neurophysiol. 2001; 112(2): 241-9.
- Betterton, et al. Determining State of Consciousness from the Intracranial Electroencephalogram (IEEG) for Seizure Prediction. From Proceeding (377) Modeling, Identification, and Control. 2003; 377-201: 313-317.
- Bhattacharya, et al. Enhanced phase synchrony in the electroencephalograph gamma band for musicians while listening to music. Phys. Rev. E. 2001; 64:012902-1-4.
- Boley, et al. Training Support Vector Machine using Adaptive Clustering. 2004 SIAM International Conference on Data Mining, Apr. 22-Apr. 24, 2004. Lake Buena Vista, FL, USA. 12 pages.
- Burges, C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 1998; 2: 121-167.
- Cao, et al. Detecting dynamical changes in time series using the permutation entropy. Physical Review E. 2004; 70:046217-1-046217-7.
- Carretero-Gonzalez, et al. Scaling and interleaving of subsystem Lyapunov exponents for spatio-temporal systems. Chaos. 1999; 9(2): 466-482.
- Casdagli, et al. Characterizing nonlinearity in invasive EEG recordings from temporal lobe epilepsy. Physica D. 1996; 99 (2/3): 381-399.
- Casdagli, et al. Nonlinear Analysis of Mesial Temporal Lobe Seizures Using a Surrogate Data Technique. Epilepsia. 1995; 36, suppl. 4, pp. 142.
- Casdagli, et al. Non-linearity in invasive EEG recordings from patients with temporal lobe epilepsy. Electroencephalogr. Clin. Neurophysiol. 1997; 102(2): 98-105.
- Cerf, et al. Criticality and synchrony of fluctuations in rhythmical brain activity: pretransitional effects in epileptic patients. Biol. Cybern. 2004; 90(4): 239-55.
- Chaovalitwongse et al.; Reply to comments on “Performance of a seizure warning based on the dynamics of intracranial EEG”; Epilepsy Research, Elsevier Science Publishers, Amsterdam, NL; vol. 72; No. 1; pp. 82-84; Nov. 1, 2006.
- Chaovalitwongse, et al. EEG Classification in Epilepsy. Annals. 2004; 2 (37): 1-31.
- Chaovalitwongse, et al. Performance of a seizure warning algorithm based on the dynamics of intracranial EEG. Epilepsy Res. 2005; 64(3): 93-113.
- Chavez, et al. Spatio-temporal dynamics prior to neocortical seizures: amplitude versphase couplings. IEEE Trans. Biomed. Eng. 2003; 50(5):571-83.
- Crichton, Michael, “Terminal Man”, 1972, Ballantine Books, NY, NY, pp. 21-24, 32-33, 70-71, and 74-81.
- D'Alessandro, et al. A multi-feature and multi-channel univariate selection process for seizure prediction. Clin. Neurophysiol. 2005; 116(3): 506-16.
- D'Alessandro, et al. Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients. IEEE Trans. Biomed. Eng. 2003; 50(5): 603-15.
- Drury, et al. Seizure prediction using scalp electroencephalogram. Exp. Neurol. 2003; 184 Suppl 1: S9-18.
- Ebersole, J. S. Functional neuroimaging with EEG source models to localize epileptogenic foci noninvasively. Neurology. Available at http://www.uchospitals.edu/pdf/uch—001471.pdf. Accessed Feb. 28, 2006.
- Ebersole, J. S. In search of seizure prediction: a critique. Clin. Neurophysiol. 2005; 116(3): 489-92.
- Elbert et al. Chaos and Physiology: Deterministic Chaos in Excitable Cell Assemblies. Physiological Reviews. 1994; 74(1):1-47.
- Elger, et al. Nonlinear EEG analysis and its potential role in epileptology. Epilepsia. 2000; 41 Suppl 3: S34-8.
- Elger, et al. Seizure prediction by non-linear time series analysis of brain electrical activity. Eur. J. Neurosci. 1998; 10(2): 786-789.
- Esteller, et al. A Comparison of Waveform Fractal Dimension Algorithms. IEEE Transactions on Circuits and Systems. 2001; vol. 48(2): 177-183.
- Esteller, et al. Continuoenergy variation during the seizure cycle: towards an on-line accumulated energy. Clin. Neurophysiol. 2005; 116(3): 517-26.
- Esteller, et al. Feature Parameter Optimization for Seizure Detection/prediction. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey. Oct. 2001.
- Faul, et al. An evaluation of automated neonatal seizure detection methods. Clin. Neurophysiol. 2005; 116(7): 1533-41.
- Fein, et al. Common reference coherence data are confounded by power and phase effects. Electroencephalogr. Clin. Neurophysiol. 1988; 69:581-584.
- Fell, et al. Linear inverse filtering improves spatial separation of nonlinear brain dynamics: a simulation study. J. Neurosci. Methods. 2000; 98(1): 49-56.
- Firpi, et al. Epileptic seizure detection by means of genetically programmed artificial features. GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, vol. 1, pp. 461-466, Washington DC, USA, 2005. ACM Press.
- Fisher et al. 1999. Reassessment: Vagnerve stimulation for epilepsy, A report of the therapeutics and technology assessment subcommittee of the Academy of Neurology. Neurology.53: 666-669.
- Franaszczuk et al.; An autoregressive method for the measurement of synchronization of interictal and ictal EEG signals; Biological Cybernetics, vol. 81; No. 1; pp. 3-9; 1999.
- Gardner, A. B. A Novelty Detection Approach to Seizure Analysis from Intracranial EEG. Georgia Institute of Technology. Apr. 2004. A dissertation available at http://etd.gatech.edu/theses /available/etd-04122004-132404/unrestricted/gardner —andrew—b—200405 —phd.pdf. Accessed Feb. 28, 2006.
- Geva, et al. Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering. IEEE Trans. Biomed. Eng. 1998; 45(10): 1205-16.
- Gigola, et al. Prediction of epileptic seizures using accumulated energy in a multiresolution framework. J. Neurosci. Methods. 2004; 138(1-2): 107-111.
- Guyon, I. An introduction to variable and feature selection. Journal of Machine Learning Research. 2003; 3:1157-1182.
- Guyon, I. Multivariate Non-Linear Feature Selection with Kernel Multiplicative Updates and Gram-Schmidt Relief. BISC FLINT-CIBI 2003 Workshop. Berkeley. 2003; p. 1-11.
- Harrison, et al. Accumulated energy revised. Clin. Neurophysiol. 2005; 116(3):527-31.
- Harrison, et al. Correlation dimension and integral do not predict epileptic seizures. Chaos. 2005; 15(3): 33106-1-15.
- Hearst M. Trends & Controversies: Support Vector Machines. IEEE Intelligent Systems. 1998; 13: 18-28.
- Hively, et al. Channel-consistent forewarning of epileptic events from scalp EEG. IEEE Trans. Biomed. Eng. 2003; 50(5): 584-93.
- Hively, et al. Detecting dynamical changes in nonlinear time series. Physics Letters A. 1999; 258: 103-114.
- Hively, et al. Epileptic Seizure Forewarning by Nonlinear Techniques. ORNL/TM-2000/333 Oak Ridge National Laboratory. Nov. 2000. Available at http://computing.ornl.gov/cse—home/staff/hively/NBICradaAnnualRpt FY00.pdf. Accessed Feb. 28, 2006.
- Hjorth, B. Source derivation simplifies topographical EEG interpretation. Am. J. EEG Technol. 1980; 20: 121-132.
- Hsu, et al. A practical guide to support vector classification. Technical report, Department of Computer Science and Information Technology, National Taiwan University, 2003. Available at http://www.csie.ntu.edu.tw/˜cjlin/papers/guide/guide.pdf. Accessed Feb. 28, 2006.
- Huynh, J. A. Evaluation of Gene Selection Using Support Vector Machine Recursive Feature Elimination. Arizona State University. May 26, 2004. (28 pages).
- Huynh, J. A. Evaluation of Gene Selection Using Support Vector Machine Recursive Feature Elimination. Presentation slides. (41 pages) (May 26, 2004).
- Iasemidis, et al. Adaptive epileptic seizure prediction system. IEEE Trans. Biomed. Eng. 2003; 50(5):616-27.
- Iasemidis, et al. Automated Seizure Prediction Paradigm. Epilepsia. 1998; vol. 39, pp. 56.
- Iasemidis, et al. Chaos Theory and Epilepsy. The Neuroscientist. 1996; 2:118-126.
- Iasemidis, et al. Comment on “Inability of Lyapunov exponents to predict epileptic seizures.” Physical Review Letters. 2005; 94(1):019801-1.
- Iasemidis, et al. Detection of the Preictal Transition State in Scalp-Sphenoidal EEG Recordings. American Clinical Neurophysiology Society Annual Meeting, Sep. 1996. pp. C206.
- Iasemidis, et al. Dynamical Interaction of the Epileptogenic Focwith Extrafocal Sites in Temporal Lobe Epilepsy (TLE). Ann. Neurol.1997; 42, pp. 429. pp. M146.
- Iasemidis, et al. Epileptogenic FocLocalization by Dynamical Analysis of Interictal Periods of EEG in Patients with Temporal Lobe Epilepsy. Epilepsia. 1997; 38, suppl. 8, pp. 213.
- Iasemidis, et al. Localizing Preictal Temporal Lobe Spike Foci Using Phase Space Analysis. Electroencephalography and Clinical Neurophysiology. 1990; 75, pp. S63-S64.
- Iasemidis, et al. Long-term prospective on-line real-time seizure prediction. Clin. Neurophysiol. 2005; 116(3):532-44.
- Iasemidis, et al. Long-Time-Scale Temporo-spatial Patterns of Entrainment of Preictal Electrocorticographic Data in Human Temporal Lobe Epilepsy. Epilepsia. 1990; 31(5):621.
- Iasemidis, et al. Measurement and Quantification of Spatio-Temporal Dynamics of Human Epileptic Seizures. In: Nonlinear Signal Processing in Medicine, Ed. M. Akay, IEEE Press. 1999; pp. 1-27.
- Iasemidis, et al. Modelling of ECoG in temporal lobe epilepsy. Biomed. Sci. Instrum. 1988; 24: 187-93.
- Iasemidis, et al. Nonlinear Dynamics of EcoG Data in Temporal Lobe Epilepsy. Electroencephalography and Clinical Neurophysiology. 1998; 5, pp. 339.
- Iasemidis, et al. Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topogr. 1990; 2(3): 187-201.
- Iasemidis, et al. Preictal Entrainment of a Critical Cortical Mass is a Necessary Condition for Seizure Occurrence. Epilepsia. 1996; 37, suppl. 5. pp. 90.
- Iasemidis, et al. Preictal-Postictal Versus Postictal Analysis for Epileptogenic Focus Localization. J. Clin. Neurophysiol. 1997; 14, pp. 144.
- Iasemidis, et al. Quadratic binary programming and dynamic system approach to determine the predictability of epileptic seizures. Journal of Combinatorial Optimization. 2001; 5: 9-26.
- Iasemidis, et al. Quantification of Hidden Time Dependencies in the EEG within the Framework of Non-Linear Dynamics. World Scientific. 1993; pp. 30-47.
- Iasemidis, et al. Spatiotemporal dynamics of human epileptic seizures. World Scientific. 1996; pp. 26-30.
- Iasemidis, et al. Spatiotemporal Evolution of Dynamical Measures Precedes Onset of Mesial Temporal Lobe Seizures. Epilepsia. 1994; 358, pp. 133.
- Iasemidis, et al. Spatiotemporal Transition to Epileptic Seizures: A Nonlinear Dynamical Analysis of Scalp and Intracranial EEG Recordings. (In Silva, F.L. Spatiotemporal Models in Biological and Artificial Systems. Ohmsha IOS Press. 1997; 37, pp. 81-88.).
- Iasemidis, et al. The evolution with time of the spatial distribution of the largest Lyapunov exponent on the human epileptic cortex. World Scientific. 1991; pp. 49-82.
- Iasemidis, et al. The Use of Dynamical Analysis of EEG Frequency Content in Seizure Prediction. American Electroencephalographic Society Annual Meeting, Oct. 1993.
- Iasemidis, et al. Time Dependencies in Partial Epilepsy. 1993; 34, pp. 130-131.
- Iasemidis, et al. Time dependencies in the occurrences of epileptic seizures. Epilepsy Res. 1994; 17(1): 81-94.
- Iasemidis, L. D. Epileptic seizure prediction and control. IEEE Trans. Biomed. Eng. 2003; 50(5):549-58.
- Jerger, et al. Early seizure detection. Journal of Clin. Neurophysiol. 2001; 18 (3):259-68.
- Jerger, et al. Multivariate linear discrimination of seizures. Clin. Neurophysiol. 2005; 116(3):545-51.
- Jouny, et al. Characterization of epileptic seizure dynamics using Gabor atom density. Clin. Neurophysiol. 2003; 114(3):426-37.
- Jouny, et al. Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period? Clin. Neurophysiol. 2005; 116(3):552-8.
- Le Van Quyen, et al. Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings. Neuroreport. 1999; 10(10):2149-55.
- Le Van Quyen, et al. Author's second reply. The Lancet. 2003; 361:971.
- Le Van Quyen, et al. Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony. J. Neurosci. Methods. 2001; 111(2):83-98.
- Le Van Quyen, et al. Nonlinear analyses of interictal EEG map the brain interdependences in human focal epilepsy. Physica D. 1999; 127:250-266.
- Le Van Quyen, et al. Preictal state identification by synchronization changes in long-term intracranial EEG recordings. Clin. Neurophysiol. 2005; 116(3):559-68.
- Le Van Quyen, M. Anticipating epileptic seizures: from mathematics to clinical applications. C. R. Biol. 2005; 328(2):187-98.
- Lehnertz, et al. Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. J. Clin. Neurophysiol. 2001; 18(3):209-22.
- Lehnertz, et al. Seizure prediction by nonlinear EEG analysis. IEEE Eng. Med. Biol. Mag. 2003; 22(1):57-63.
- Lehnertz, et al. The First International Collaborative Workshop on Seizure Prediction: summary and data description. Clin. Neurophysiol. 2005; 116(3):493-505.
- Lehnertz, K. Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy—an overview. Int. J. Psychophysiol. 1999; 34(1):45-52.
- Latka, et al. Wavelet analysis of epileptic spikes. Phys. Rev. E. 2003; 67 (5 Pt 1):052902 (6 pages).
- Lemos, et al. The weighted average reference montage. Electroencephalogr. Clin. Neurophysiol. 1991; 79(5):361-70.
- Li, et al. Fractal spectral analysis of pre-epileptic seizures in terms of criticality. J. Neural Eng. 2005; 2(2):11-16.
- Li, et al. Linear and nonlinear measures and seizure anticipation in temporal lobe epilepsy. J. Comput. Neurosci. 2003; 15(3):335-45.
- Li, et al. Non-linear, non-invasive method for seizure anticipation in focal epilepsy. Math. Biosci. 2003; 186(1):63-77.
- Litt, et al. Prediction of epileptic seizures. Lancet Neurol. 2002; 1(1):22-30.
- Litt, et al. Seizure prediction and the preseizure period. Curr. Opin. Neurol. 2002; 15(2):173-7.
- Maiwald, et al. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D. 2004; 194:357-368.
- Mangasarian, et al. Lagrangian Support Vector Machines. Journal of Machine Learning Research. 2001; 1:161-177.
- Martinerie, et al. Epileptic seizures can be anticipated by non-linear analysis. Nat. Med. 1998; 4(10):1173-6.
- McSharry, et al. Comparison of predictability of epileptic seizures by a linear and a nonlinear method. IEEE Trans. Biomed. Eng. 2003; 50(5):628-33.
- McSharry, et al. Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings. Med. Biol. Eng. Comput. 2002; 40(4):447-61.
- McSharry, P. E. Detection of dynamical transitions in biomedical signals using nonlinear methods. Lecture Notes in Computer Science 2004; 3215:483-490.
- Meng, et al. Gaussian mixture models of ECoG signal features for improved detection of epileptic seizures. Med. Eng. Phys. 2004; 26(5):379-93.
- Mizuno-Matsumoto, et al. Wavelet-crosscorrelation analysis can help predict whether bursts of pulse stimulation will terminate after discharges. Clin. Neurophysiol. 2002; 113(1):33-42.
- Mormann et al.; Seizure prediction: the long and winding road; Brain; vol. 130; No. 2; pp. 314-333; Sep. 28, 2006.
- Mormann, et al. Automated detection of a preseizure state based on a decrease in synchronization in intracranial electroencephalogram recordings from epilepsy patients. Phys. Rev. E. 2003; 67(2 Pt 1):021912-1-10.
- Mormann, et al. Epileptic seizures are preceded by a decrease in synchronization. Epilepsy Res. 2003; 53(3):173-85.
- Mormann, et al. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D. 2000; 144:358-369.
- Mormann, et al. On the predictability of epileptic seizures. Clin. Neurophysiol. 2005; 116(3):569-87.
- Mormann, et al. Seizure anticipation: from algorithms to clinical practice. Curr. Opin. Neurol. 2006; 19(2):187-93.
- Navarro, et al. Seizure anticipation in human neocortical partial epilepsy. Brain. 2002; 125:640-55.
- Navarro, et al. Seizure anticipation: do mathematical measures correlate with video-EEG evaluation? Epilepsia. 2005; 46(3):385-96.
- Niederhauser, et al. Detection of seizure precursors from depth-EEG using a sign periodogram transform. IEEE Trans. Biomed. Eng. 2003; 50(4):449-58.
- Nigam, et al. A neural-network-based detection of epilepsy. Neurological Research. 2004; 26(1):55-60.
- Osorio, et al. Automated seizure abatement in humans using electrical stimulation. Ann. Neurol. 2005; 57(2):258-68.
- Osorio, et al. Performance reassessment of a real-time seizure-detection algorithm on long ECoG series. Epilepsia. 2002; 43(12):1522-35.
- Osorio, et al. Real-time automated detection and quantitative analysis of seizures and short-term prediction of clinical onset. Epilepsia. 1998; 39(6):615-27.
- Ossadtchi, et al. Hidden Markov modelling of spike propagation from interictal MEG data. Phys. Med. Biol. 2005; 50(14):3447-69.
- Pflieger, et al. A noninvasive method for analysis of epileptogenic brain connectivity. Presented at the American Epilepsy Society 2004 Annual Meeting, New Orleans. Dec. 6, 2004. Epilepsia. 2004; 45(Suppl. 7):70-71.
- Pittman, V. Flexible Drug Dosing Produces Less Side-effects in People With Epilepsy. Dec. 29, 2005. Available at http://www.medicalnewstoday.com/medicalnews.php?newsid=35478. Accessed on Apr. 17, 2006.
- Platt, et al. Large Margin DAGs for Multiclass Classification. S.A. Solla. T.K. Leen adn K. R. Muller (eds.). 2000; pp. 547-553.
- Platt, J. C. Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. Advances in Neural Information Processing Systems. 1999; 11:557-563.
- Protopopescu, et al. Epileptic event forewarning from scalp EEG. J. Clin. Neurophysiol. 2001; 18(3):223-45.
- Rahimi, et al. On the Effectiveness of Aluminum Foil Helmets: An Empirical Study. Available at http://people.csail.mit.edu/rahimi/helmet/. Accessed Mar. 2, 2006.
- Rothman et al.; Local Cooling: a therapy for intractable neocortical epilepsy; Epilepsy Currents; vol. 3; No. 5; pp. 153-156; Sep./Oct. 2003.
- Robinson, et al. Steady States and Global Dynamics of Electrical Activity in the Cerebral Cortex. Phys. Rev. E. 1998; (58):3557-3571.
- Rudrauf, et al. Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals. Neurolmage. 2005. (19 pages.).
- Saab, et al. A system to detect the onset of epileptic seizures in scalp EEG. Clin. Neurophysiol, 2005; 116:427-442.
- Sackellares et al. Computer-Assisted Seizure Detection Based on Quantitative Dynamical Measures. American Electroencephalographic Society Annual Meeting, Sep. 1994.
- Sackellares et al. Dynamical Studies of Human Hippocampin Limbic Epilepsy. Neurology. 1995; 45, Suppl. 4, pp. A 404.
- Sackellares et al. Epileptic Seizures as Neural Resetting Mechanisms. Epilepsia. 1997; vol. 38, Sup. 3.
- Sackellares et al. Measurement of Chaos to Localize Seizure Onset. Epilepsia. 1989; 30(5):663.
- Sackellares et al. Relationship Between Hippocampal Atrophy and Dynamical Measures of EEG in Depth Electrode Recordings. American Electroencephalographic Society Annual Meeting, Sep. 1995. pp. A105.
- Sackellares et al.; Predictability analysis for an automated seizure prediction algorithm; Journal of Clinical Neurophysiology; vol. 23; No. 6; pp. 509-520; Dec. 2006.
- Sackellares, J. C. Epilepsy—when chaos fails. In: chaos in the brain? Eds. K. Lehnertz & C.E. Eiger. World Scientific. 2000 (22 pages).
- Salant, et al. Prediction of epileptic seizures from two-channel EEG. Med. Biol. Eng. Comput. 1998; 36(5):549-56.
- Schelter et al.; Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction; Chaos; vol. 16; pp. 013108-1-10; Jan. 2006.
- Schelter, et al. Testing for directed influences among neural signals using partial directed coherence. J. Neurosci. Methods. 2006; 152(1-2):210-9.
- Schindler, et al. EEG analysis with simulated neuronal cell models helps to detect pre-seizure changes. Clin. Neurophysiol. 2002; 113(4):604-14.
- Schwartzkroin, P. Origins of the Epileptic State. Epilepsia. 1997; 38, supply. 8, pp. 853-858.
- Sheridan, T. Humans and Automation. NY: John Wiley. 2002.
- Shoeb et al. Patient-specific seizure detection. MIT Computer Science and Artificial Intelligence Laboratory. 2004; pp. 193-194.
- Snyder et al; The statistics of a practical seizure warning system; Journal of Neural Engineering; vol. 5; pp. 392-401; 2008.
- Staba, et al. Quantitative analysis of high-frequency oscillations (80-500 Hz) recorded in human epileptic hippocampand entorhinal cortex. J. Neurophysiol. 2002; 88(4):1743-52.
- Stefanski, et al. Using chaos synchronization to estimate the largest Lyapunov exponent of nonsmooth systems. Discrete Dynamics in Nature and Society. 2000; 4:207-215.
- Subasi, et al. Classification of EEG signals using neural network and logistic regression. Computer Methods Programs Biomed. 2005; 78(2):87-99.
- Szoka et al. Procedure for preparation of liposomes with large internal aqueospace and high capture volume by reverse phase evaporation. 1978. Proc. Natl Acad. Sci. USA. 75: 4194-4198.
- Tass, et al. Detection of n: m Phase Locking from Noisy Data: Application to Magnetoencephalography. Physical Review Letters. 1998; 81(15):3291-3294.
- Terry, et al. An improved algorithm for the detection of dynamical interdependence in bivariate time-series. Biol. Cybern. 2003; 88(2):129-36.
- Tetzlaff, et al. Cellular neural networks (CNN) with linear weight functions for a prediction of epileptic seizures. Intl. J. of Neural Systems. 2003; 13(6):489-498.
- Theiler, et al. Testing for non-linearity in time series: the method of surrogate data. Physica D. 1992; 58:77-94.
- Tsakalis, K. S. Prediction and control of epileptic seizures: Coupled oscillator models. Arizona State University. (Slide: 53 pages) (No date).
- Van Drongelen, et al. Seizure anticipation in pediatric epilepsy: use of Kolmogorov entropy. Pediatr. Neurol. 2003; 29(3): 207-13.
- Van Putten, M. Nearest neighbor phase synchronization as a measure to detect seizure activity from scalp EEG recordings. J. Clin. Neurophysiol. 2003; 20(5):320-5.
- Venugopal, et al. A new approach towards predictability of epileptic seizures: KLT dimension. Biomed Sci. Instrum. 2003; 39:123-8.
- Vonck, et al. Long-term amygdalohippocampal stimulation for refractory temporal lobe epilepsy. Ann. Neurol. 2002; 52(5):556-65.
- Vonck, et al. Long-term deep brain stimulation for refractory temporal lobe epilepsy. Epilepsia. 2005; 46(Suppl 5):98-9.
- Vonck, et al. Neurostimulation for refractory epilepsy. Acta. Neurol. Belg. 2003; 103(4):213-7.
- Weiss, P. Seizure prelude found by chaos calculation. Science News. 1998; 153(20):326.
- Wells, R. B. Spatio-Temporal Binding and Dynamic Cortical Organization: Research Issues. Mar. 2005. Available at http://www.mrc.uidaho.edu/˜rwells/techdocs/Functional%20Column%20Research%20Issues.pdf. Accessed Mar. 2, 2006.
- Widman, et al. Reduced signal complexity of intracellular recordings: a precursor for epileptiform activity? Brain Res. 1999; 836(1-2):156-63.
- Winterhalder, et al. Sensitivity and specificity of coherence and phase synchronization analysis. (In Press) Phys. Lett. A. 2006.
- Winterhalder, et al. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav. 2003; 4(3):318-25.
- Wong et al.; A stochastic framework for evaluating seizure prediction algorithms using hiden markov models; Journal of Neurophysiology; vol. 97, No. 3; pp. 2525-2532; Oct. 4, 2006.
- Yang et al.; Testing whether a prediction scheme is better than guess; Ch. 14 in Quantitative Neuroscience: Models, Algorithms, Diagnostics, and Therapeutic Applications; pp. 251-262; 2004.
- Yang, et al. A supervised feature subset selection technique for multivariate time series. Available at http://infolab.usc.edu/DocsDemos/fsdm05.pdf. Accessed Mar. 2, 2006.
- Yang, et al. CLe Ver: A feature subset selection technique for multivariate time series. T. B. Ho, D. Cheung, and H. Liu (Eds.): PAKDD. 2005; LNAI 3518: 516-522.
- Yang, et al. Relation between Responsiveness to Neurotransmitters and Complexity of Epileptiform Activity in Rat Hippocampal CA1 Neurons. Epilepsia. 2002; 43(11):1330-1336.
- Yatsenko, et al. Geometric Models, Fiber Bundles, and Biomedical Applications. Proceedings of Institute of Mathematics of NAS of Ukraine. 2004; 50 (Part 3):1518R1525.
- Zaveri et al. Time-Frequency Analyses of Nonstationary Brain Signals. Electroencephalography and Clinical Neurophysiology. 1991; 79, pp. 28P-29P.
- Zhang, et al. High-resolution EEG: cortical potential imaging of interictal spikes. Clin. Neurophysiol. 2003; 114(10):1963-73.
- DiLorenzo, Daniel, U.S. Appl. No. 11/282,317 entitled “Closed-loop vagus nerve stimulation,” filed Nov. 17, 2005.
- Himes et al.; U.S. Appl. No. 12/716,132 entitled “Displaying and Manipulating Brain Function Data Including Enhanced Data Scrolling Functionality,” filed Mar. 2, 2010.
- Himes et al.; U.S. Appl. No. 12/716,147 entitled “Displaying and Manipulating Brain Function Data Including Filtering of Annotations,” filed Mar. 2, 2010.
- Higgins et al.; U.S. Appl. No. 13/026,961 entitled “Neurological monitoring and alerts,” filed Feb. 14, 2011.
- Chen et al.; Clinical utility of video-EEG monitoring; Perdiatric Neurology; vol. 12; No. 3; pp. 220-224; 1995.
Filed: Mar 23, 2011
Date of Patent: Apr 18, 2017
Patent Publication Number: 20110213222
Assignee: CYBERONICS, INC. (Houston, TX)
Inventors: Kent W. Leyde (Sammamish, WA), John F. Harris (Bellevue, WA)
Primary Examiner: Christine H Matthews
Assistant Examiner: Joshua D Lannu
Application Number: 13/070,333
International Classification: A61B 5/00 (20060101); A61B 5/04 (20060101); A61B 5/0476 (20060101); A61M 5/142 (20060101); A61N 1/36 (20060101); A61N 1/372 (20060101); G06F 19/00 (20110101);